# Sedai: Blog (part 18 of 27) Full content of 10 pages from https://sedai.io/blog, published 2026-04-16 to 2026-05-12. Only pages published in the last 12 months are included. Each entry starts with the page title and its source URL. Every full-content file is listed in https://sedai.io/llms.txt. --- # Kubernetes ConfigMap Usage, Examples, and the Production Drift Problem Source: https://sedai.io/blog/kubernetes-configmap Learn what Kubernetes ConfigMaps are, how to use them, and explore real-world examples to manage app configs efficiently in your clusters. You treat a Kubernetes ConfigMap like static configuration. Kubernetes treats it as live input to your application's behavior. That mismatch is how stale resource assumptions stay in production for months, long after the workload they configure has moved on. Most teams ship a ConfigMap, wire it into a Deployment, version it in Git, and move on. The workload keeps running. The configuration drifts. The resource profile the pod was tuned for goes quietly stale, and no one notices until the pager fires or the cloud bill shifts. This guide covers the mechanics, the practical patterns, and the production problem most teams never frame as a ConfigMap problem. **In this article:** - [What Is a Kubernetes ConfigMap?](https://sedai.io/blog/kubernetes-configmap#:~:text=ConfigMap%2DDriven%20Drift-,What%20Is%20a%20Kubernetes%20ConfigMap%3F,-A%20ConfigMap%20is) - [How to Use a ConfigMap](https://sedai.io/blog/kubernetes-configmap#:~:text=first%20three%20patterns.-,How%20to%20Use%20a%20ConfigMap,-A%20ConfigMap%20is) - [Practical ConfigMap Examples](https://sedai.io/blog/kubernetes-configmap#:~:text=during%20incident%20review.-,Practical%20ConfigMap%20Examples,-ConfigMap%20examples%20matter) - [Where ConfigMaps Breaks Down in Production](https://sedai.io/blog/kubernetes-configmap#:~:text=Book%20a%20Demo-,Where%20ConfigMaps%20Breaks%20Down%20in%20Production,-The%20ConfigMap%20model) - [Treating ConfigMaps as Optimization Signals](https://sedai.io/blog/kubernetes-configmap#:~:text=match%20the%20workload%3F-,Treating%20ConfigMaps%20as%20Optimization%20Signals,-A%20ConfigMap%20change) - [What Teams Should Do After a ConfigMap Change?](https://sedai.io/blog/kubernetes-configmap#:~:text=the%20system%20changes.-,What%20Teams%20Should%20Do%20After%20a%20ConfigMap%20Change%3F,-ConfigMap%20hygiene%20belongs) - [How Sedai Handles ConfigMap-Driven Drift](https://sedai.io/blog/kubernetes-configmap#:~:text=that%20consume%20it.-,How%20Sedai%20Handles%20ConfigMap%2DDriven%20Drift,-The%20previous%20section) ## **What Is a Kubernetes ConfigMap?**  A ConfigMap is a Kubernetes API object that stores non-sensitive configuration as key-value pairs for pods and other objects to consume at runtime. It decouples environment-specific configuration from the container image, so teams can change log levels, feature flags, heap settings, connection pools, or file-based application settings without rebuilding the image. ConfigMaps are not encrypted, so credentials, tokens, and passwords belong in Secrets or another secure store. The [Kubernetes ConfigMap concepts reference](https://kubernetes.io/docs/concepts/configuration/configmap/) also defines operational constraints that matter in production: ConfigMaps have a 1 MiB data limit, pod references must stay in the same namespace, and how the application sees a changed value depends on how the pod consumes the value. Pods usually consume ConfigMaps in three ways: - As **environment variables** loaded when the container starts - As **files projected** through a *read-only* volume mount - As **command-line arguments** that reference ConfigMap-backed environment variables Applications can also read ConfigMaps directly through the Kubernetes API, but that pattern requires application code to handle watch behavior and namespace access. Most platform teams rely on the first three patterns. Kubernetes management gets easier when optimization runs itself. [Book a demo](https://sedai.io/demo) to see how Sedai reduces toil across your clusters without adding operational overhead. ## **How to Use a ConfigMap** A ConfigMap is useful only after you decide how the application reads configuration. The pattern determines where the value appears inside the container and what has to happen before a changed value reaches a running workload. ### Environment Variables Environment variables copy ConfigMap keys into the container's environment when the pod starts. Engineers use this pattern for applications that read settings through os.Getenv() or the equivalent in their runtime, such as `LOG_LEVEL, JAVA_OPTS, FEATURE_FLAG_MODE, or DB_POOL_SIZE`. Updates to the ConfigMap do not change environment variables inside an already running container. To pick up the new values, the pod must restart or roll out again. `envFrom:`   `- configMapRef:`       `name: app-config` ### Volume Mounts Volume mounts expose ConfigMap data as files inside the pod. Kubernetes projects each key as a file name and each value as the file contents under the mount path. This pattern fits applications that already expect configuration files on disk, such as Nginx reading a server block or a Java service reading application.properties. Mounted ConfigMaps can update without restarting the pod, but the application still has to re-read the file. A subPath mount will not receive ConfigMap updates. `volumes:`   `- name: config`     `configMap:`       `name: app-config` `volumeMounts:`   `- name: config`     `mountPath: /etc/app` ### Command-Line Arguments Command-line arguments use ConfigMap values when the application is driven by startup flags. Kubernetes does this by first loading a ConfigMap value into an environment variable, then referencing that variable in the container's args field with $(VAR\_NAME). This is less common because arguments are fixed when the container starts. It fits Jobs, batch tools, and small services whose behavior is controlled by startup flags rather than a runtime configuration file. ### How ConfigMap Changes Reach Running Pods | Pattern | Ideal Use Case | How the Application Sees a Changed Value | | --- | --- | --- | | Environment variables | Apps using os.Getenv() or equivalent startup reads | The pod must restart | | Volume mounts | File-based configuration readers | kubelet eventually projects updates, but the app must re-read the file | | Command-line arguments | CLI tools, Jobs, and flag-driven workloads | The pod must restart | The volume-mount case is the only one that can update a running pod without a restart. Even then, propagation depends on the kubelet sync cycle and cache behavior, so the new file contents may not appear immediately. During a rollout, pods can also run old and new values side by side, which makes configuration state easy to miss during incident review. ## **Practical ConfigMap Examples** ConfigMap examples matter because small configuration edits can change the workload's resource shape. These are not just cleaner ways to avoid hard-coded values. A typical JVM workload reads heap settings from an environment variable. The ConfigMap might hold `JAVA_OPTS:"-Xms2g -Xmx2g"`, and the JVM starts with a 2 GB heap. If an engineer changes that value to -Xmx4g during a load test, the container image stays the same but the runtime memory envelope doubles. If the pod's memory request and limit stay tuned for the old value, OOMKills become likely. A worker service has the same problem with connection pools. If `DB_POOL_SIZE`:20 becomes `DB_POOL_SIZE`:100 before a traffic event, each additional connection consumes memory and a file descriptor. The Deployment spec may still show the same resource request, but the process inside the pod now behaves differently. For feature-flagged workloads, a volume-mounted ConfigMap might hold features.yaml so the app can watch for runtime changes. At scale, marking that ConfigMap immutable is often safer: it prevents accidental background updates, reduces API server watch load, and forces a redeploy when behavior changes. All three examples share the same blind spot. Kubernetes records that configuration changed, but it does not know whether the pod's resource policy, restart behavior, or scaling rules still match the new application behavior. ### Hidden ConfigMap Drift Costs That Escalate Fast See how Sedai uncovers stale resource tuning, idle capacity & overprovisioning—and continuously optimizes Kubernetes workloads before costs escalate. ## **Where ConfigMaps Breaks Down in Production** The ConfigMap model is simple: store configuration outside the image and let pods consume it. Production is less simple because configuration often changes how a process uses CPU, memory, sockets, and startup time. When an engineer edits `JAVA_OPTS`from `-Xmx2g to -Xmx4g`, the pod may now need different resource requests & limits than the ones it was[ rightsized for](https://sedai.io/blog/optimizing-workload-rightsizing-in-amazon-eks-for-efficient-resource-utilization). Kubernetes does not re-tune those values because a ConfigMap changed. The same issue applies to autoscaling. If the [Horizontal Pod Autoscaler](https://sedai.io/blog/hpa-kubernetes) is configured around CPU utilization, it may keep reacting to CPU while the failure mode has moved to memory pressure. Scaling more pods does not fix a per-pod memory envelope that is now too small. This is configuration drift in operational form: the value changed, but the resource model around the workload did not. Even when a platform team spots the drift, manually re-rightsizing every affected workload does not scale past a small service count. The[ CNCF's FinOps microsurvey](https://www.cncf.io/blog/2024/04/29/finops-for-kubernetes-engineering-cost-optimization/) found that 70% of organizations attribute rising Kubernetes cloud costs to over-provisioning. ConfigMap drift is one reason those numbers stay high despite active optimization work: teams keep tuning resources against behavior that has already changed. Threshold-based autoscalers cannot close this gap by themselves. They react after a metric crosses a line. They do not trigger the upstream question a ConfigMap change should raise: do the requests, limits, restart policy, and scaling bounds still match the workload? ## **Treating ConfigMaps as Optimization Signals** A ConfigMap change should trigger a targeted re-evaluation of every workload that consumes it. Use the change as a cue to compare how the workload behaved before and after the new value reached the application. That review should look at latency, errors, traffic, saturation, restarts, OOMKills, CPU, memory, and scaling events. If those signals move, the resource policy should be rechecked against the new envelope. [Application-aware optimization](https://sedai.io/blog/kubernetes-resource-optimization-guide) starts from that distinction. A threshold rule sees that memory crossed a configured line. An application-aware system asks whether the workload's behavior changed enough to justify a safer request, a higher limit, a different restart policy, or a scaling adjustment. Sedai's decision engine evaluates those changes incrementally. It reads [golden signals](https://sedai.io/blog/understanding-and-improving-service-reliability), checks whether the shift is meaningful, applies small changes when action is safe, and keeps validating after the change. Sedai has eight U.S. patents focused on production safety and has run more than [100,000 autonomous operations with zero incidents](https://tfir.io/the-self-driving-cloud-is-real-sedai-has-run-100k-ops-without-incident/). That is the operational difference between automation and autonomy. Automation runs a predefined step when a condition appears. Autonomy evaluates live behavior, decides whether action is safe, and keeps watching after the system changes. ConfigMap misconfigurations and etcd bloat create operational risks that compound as clusters grow. [Book a demo](https://sedai.io/demo) to see how Sedai reduces Kubernetes management toil. ## **What Teams Should Do After a ConfigMap Change?** ConfigMap hygiene belongs under optimization because configuration changes often alter resource demand. A clean YAML diff is not enough when the consuming process now uses more memory, holds more connections, or reloads behavior at runtime. After a ConfigMap change, teams should ask four questions: - Which workloads consume this ConfigMap? - Did those workloads restart, reload, or keep the old value? - Did latency, errors, saturation, restarts, or resource usage move? - Do requests, limits, and scaling policies still match the observed workload? Manual re-tuning fails because this handoff is usually invisible. A configuration PR merges, the consuming pods reload or restart, and the resource model stays where it was. [McKinsey's FinOps-as-code work](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/everything-is-better-as-code-using-finops-to-manage-cloud-costs) makes the broader operational point: cloud efficiency improves when optimization decisions are embedded in engineering workflows. For ConfigMaps, that means every meaningful change should trigger telemetry review and resource-policy re-evaluation for the workloads that consume it. ## **How Sedai Handles ConfigMap-Driven Drift** The previous section is the operating model. Sedai runs that loop continuously, without waiting for an engineer to remember that a ConfigMap changed. Sedai reads live golden signals from each workload, detects when application behavior has shifted, and re-tunes resource requests incrementally against SLO-bounded safety checks. It evaluates observed behavior before acting, instead of firing a rightsizer whenever a ConfigMap changes. [KnowBe4 cut AWS costs by 27%](https://sedai.io/blog/knowbe4) using Sedai's autonomous optimization while continuing to scale. That result follows from the same mechanism this article is arguing for: small changes, continuously verified against workload behavior, with no static rules that break when configuration changes. If your team is still manually re-rightsizing after every meaningful ConfigMap change, your resource model is always one version behind. [See how Sedai handles application-aware Kubernetes optimization](https://sedai.io/blog/kubernetes-resource-optimization-guide). --- # Sedai + Archera: How We're Closing the Gaps in Your Cloud Budget Source: https://sedai.io/blog/sedai-archera-partnership-cloud-budget Sedai and Archera are partnering to optimize the entire cloud cost lifecycle, from insurance-backed commitments to the configuration of individual cloud resources. > Sedai and Archera are partnering to optimize the entire cloud cost lifecycle, from insurance-backed commitments to the configuration of individual cloud resources. For FinOps and finance teams under pressure to cut cloud spend without adding headcount; this closes two gaps that have been quietly compounding your losses. ## **The two gaps that quietly drain your cloud budget** Most organizations are losing money to two distinct problems. When stacked together, they turn the cloud budget into a leaky bucket.  **The commitment gap.** Reserved Instances and Savings Plans offer big-chunk savings, but locking into a 1–3 year term feels risky. What if the architecture changes? What if a workload is sunset? The fear is natural, but the cost of under-committing is usually higher than leaders realize. **The execution gap.** Even where teams commit well, individual resources and workloads go under-utilized. Developers will often over-provision CPU and memory on purpose, to prevent availability and performance issues when demand changes. But the result is that about 30% of cloud costs are wasted — as these teams pay for more cloud than they need. > “Commitment waste — 15–20% of committed spend going unused — isn’t just wasting money. You’re wasting the discount you already paid for.” These two gaps compound each other. You under-commit out of fear, and whenever you do commit, you under-utilize. The result is a cloud-savings program that plateaus well short of its actual potential. Commitment-based savings only work when your actual usage matches your reservations. [Book a demo](https://sedai.io/demo) to see how Sedai keeps your savings plans continuously optimized. ## **How the Archera Guarantee Works** Archera transfers commitment shortfall risk from the customer to Archera. You capture the discount; Archera carries the liability. This is the core mechanism: After a 30-day hold, if a commitment goes unused or underutilized across AWS, Azure, or Google Cloud, Archera makes you whole. We either recapture the unused portion via hyperscaler marketplaces or rebate the unrealized value directly back to you. No stranded spend. No penalty when workloads shift. [**Commitment shortfall is our liability, not yours.**](https://www.archera.ai/insured-commitments) ### **What you get vs. the native market** | Archera Guarantee | | | | --- | --- | --- | | Minimum term | 1–3 years | 30 days | | Shortfall risk | Customer | Archera | | Engineering lift | Manual RI/SP/CUD management | Zero | | Exit clause | None | 30-day | | Savings delivered | Variable (stranded commits erode) | ~40% net | ### **What this changes for finance teams:** On the native AWS, Azure, and Google Cloud, every committed dollar is a liability you're betting against workload volatility. With Archera, it's not. That inverts the decision model: - You stop under-committing to hedge against drift - You stop running 1–3 year lock-in math on quarterly-moving workloads - You stop eating stranded spend when engineering re-architects ## **Why Sedai is the perfect partner for optimizing costs** Archera removes the downside of committing across AWS, Azure, and Google Cloud. Sedai removes the utilization gap that creates the downside in the first place.  Sedai is an AI copilot that optimizes cloud costs and application performance, safely and autonomously. Its platform is patented to optimize cloud resources — including compute, storage, data, and AI/ML workloads — without ever causing an incident in production. Customers typically see a 30%+ reduction in cloud costs, with zero manual toil for their SREs. Sedai’s secret sauce is application awareness. The [Sedai platform](https://sedai.io/platform) uses machine learning to understand the actual behavior of your applications, in real time, which allows it to dynamically right-size cloud resources. This intelligence layer makes sure you’re getting the maximum value from your cloud commitments. ### **Together:** - Archera makes it safe to commit aggressively - Sedai ensures those commitments are fully utilized - Neither achieves compounding savings without the other Together, we cover the entire cloud cost lifecycle.  - **\~46%** Blended savings over your own-book cloud spend compared with on-demand pricing. - **30–40%** Typical savings delivered to Sedai customers after all optimizations. - **30 days** Minimum commit period to unlock the Archera guarantee. When Archera maximizes your commitment discount and Sedai maximizes the utilization of those commitments, savings stack across both layers. The end result: it’s not uncommon to see a \~75% commitment discount applied to workloads that run right-sized by 20% autonomous — a dramatically different number than either optimization would achieve alone. And crucially: these savings don’t require additional headcount, don’t come from over-commit-or-miss trade-offs, and don’t require shipping risk or pressure to manage all the optimization tradeoffs. ### Start Saving on Your Cloud Budget Book a demo to see how Sedai + Archera can dramatically lower your cloud spend. ## **Who this is built for** **Teams under committing under-buy:** Organizations using 10–40% of potential savings on the table because committed risk feels too high. Archera guarantees the commitment. Sedai ensures a flexibility high enough to justify committing more. **K8s and DevOps teams drowning in toil:** Engineering organizations where 30–40% of toil, time goes in a small time, going, reading reviews, and tuning resource manifests — work that should be either done and not required in real time. **Enterprises with aggressive accounting targets:** Teams of FY25, AWS, or GCP engaging with cost- inscribing MTR/SUB/bills and right-sizing across hundreds of services every period. Seeing it handles the commitment layer, Sedai scales every single target value. **Engineering-only FinOps teams:** Organizations without central economics to play, end engineering — cloud management is often offloaded to developers with no central tooling to purchase commitment volume. Archera handles the structure. Sedai handles the execution. **Multi-cloud governance leaders:** Organizations managing several with AWS, Azure, and/or Google Cloud who need unified dashboards and simple, credible tools for use. “When financial tooling can pay for itself, reports will open up it.” The Sedai and Archera partnership gives engineering teams a unified path from workload baseline analysis to optimized commitment strategy — without managing two separate workflows. [Book a demo](https://sedai.io/demo) to see how the combined platform applies to your environment. ### **Getting started** The rollout is designed to deliver value quickly and compound over time.  - Archera can generate a risk-free savings plan within 72 hours of onboarding.  - Sedai runs a two-week Proof of Value  analysis, moving and oppressing the next optimization without customer suggestions for you by the ROI on site.From there, savings stack.  - Archera manages the full commitment lifecycle automatically at your spend context. Sedai continuously right-sizes workloads across AWS, Azure, and Google Cloud. > “Your FinOps program stops producing slideware and starts autonomously compounding ROI.” ## **Ready to close both gaps?** Talk to your Sedai or Archera account team to start a no-cost savings assessment. [**Book a demo with Sedai](https://sedai.io/demo)   ·  [Book a demo with Archera](https://www.archera.ai/demo) --- # Using Amazon ECS Spot, Savings Plan and Reserved Instances to Optimize Costs Source: https://sedai.io/blog/using-amazon-ecs-spot-savings-plan-and-reserved-instances-to-optimize-costs Discover AWS pricing models for ECS in this guide, covering On-Demand, Reserved Instances, Savings Plans, and Spot Instances. Learn how On-Demand instances offer a flexible pay-as-you-go model for fluctuating workloads, and explore the long-term discounts available with Reserved Instances and Savings Plans for predictable usage. Delve into Spot Instances, which provide steep discounts for fault-tolerant or stateless applications. This article also explains how to optimize costs and ensure availability by mixing On-Demand and Spot capacities, with a focus on ECS’s capabilities in automating Spot Instance lifecycle management and integrating with AWS Auto Scaling Groups. Perfect for optimizing your AWS spending strategy. ## Summary This article explains how you can use Amazon ECS spot, savings plans and reserved instances to [optimize cloud costs](https://www.sedai.io/blog/cloud-cost-management-and-optimization-best-practices). - There are four AWS pricing models: On-Demand, [Reserved Instances](https://www.sedai.io/blog/time-based-vs-usage-based-pricing-for-compute), Savings Plans, and Spot Instances, each tailored to different usage patterns and budgetary constraints. - Spot Instances can achieve up to 90% savings compared to On-Demand rates, while effectively managing risks associated with their potential interruptions. - Combining On-Demand and [Spot Instances](https://www.sedai.io/blog/how-to-test-sedais-autonomous-optimization-with-an-aws-severless-demo-application) within ECS using Auto Scaling Groups to dynamically adjust to workload demands, ensuring optimal cost efficiency and service availability. ECS cost optimization requires more than scaling policies. [Book a demo](https://sedai.io/demo) to see how Sedai manages task rightsizing, spot usage, and idle resource cleanup in one platform. - Reserved Instances and [Savings Plans](https://www.sedai.io/blog/three-major-levers-to-optimize-ecs-costs) can provide discounts for long-term commitments based on your consistent workload requirements. - Implement a comprehensive purchasing strategy that incorporates all available options, optimizing cost efficiency while mitigating risks like Spot interruptions or overcommitments. ## **AWS Pricing Models**  Compute in AWS is primarily offered with four purchasing options: - On-Demand - Reserved Instances - Savings Plans - Spot Instances On-demand instances follow the standard pay-as-you-go model, where you are billed by the second. This does not require any long term commitments, free’s you from the complexities of planning and purchasing compute, and are best suited for highly available fluctuating workloads. Reserved Instances and Savings Plans are pricing models, where you commit to long term compute usage (usually 1 or 3 years in a more-or-less flexible way), in exchange for significant discounts. RIs and Savings Plans are perfect for workloads with steady usage or to handle the base load of unpredictable workloads . Spot instances constitute the spare compute capacity in AWS which are available at steep discounts of up to 90% compared to on-demand prices. These are best suited for stateless or fault-tolerant workloads as AWS can claim these instances back to serve rising demand. ## **Spot Instances** The purchasing options provided by AWS, all rely on the same underlying EC2 infrastructure which behave the same way.  Spot is no exception.  Spot instances are the idle EC2 instances that are not being used to fulfill on-demand requests, and hence are made available at cheap prices with discounts of up to 90% off compared to on-demand prices. The prices of spot instances vary with time and demand. ### Cost Optimization Rules That Balance ECS Spot & Reserved Capacity See how Sedai combines Spot Instances, Savings Plans, and Reserved Instances with ECS capacity strategies and Auto Scaling to continuously reduce costs, improve availability, and mitigate interruption risk. All these price benefits come with a catch. Spot instances can be interrupted to fulfill rising on-demand requests. The instance will be given a 2 minute notice, after which, it is terminated. Fault-tolerant or stateless workloads hence work best with spot instances, as an interruption will have little to no impact on it. ECS cost management goes beyond instance choices and pricing models. [Book a demo](https://sedai.io/demo) to see how Sedai optimizes ECS with Spot, Savings Plans and Reserved Instances together. AWS spot capacity is divided into spot instance pools. All spot instances of a specific instance type running in an AZ constitute a spot instance pool. For example: All C5.xlarge spot instances running in us-east-1a form a spot instance pool while all C5.2xlarge spot instances in the same AZ form another pool. Likewise, if you use the same instance type on three different AZ’s, you are  consuming capacity from three different spot pools. The prices and capacities of these pools fluctuate independent of each other. Tying this back to spot interruptions, the more instance pools you use, the more diverse you are, the less downtime you face, when demand increases.  The “don't put all your eggs in one basket” concept applies to spot. ‍ As of March 2024, with all the new capabilities we have, data shows us that spot interruptions have become fairly infrequent, with only 5% of spot instances interrupted in the last three months. The more we stick to best practices and proper diversification, the better spot as a whole will function. ‍ #### **Spot on ECS** ECS comes with built-in support for spot instances. Compute diversification across instance pools to automatic replacement of interrupted spot instances, are all taken care of by ECS. A task in ECS can be launched in two ways: - You can opt to run it in an EC2 instance, where you have full control over the underlying instance type and operating system.  - Or you can choose to run your ECS task in Fargate mode, where AWS relieves you from the operational burden of having to maintain servers. In both these approaches, you can opt to make use of spot instances to bring in significant cost reductions, with EC2 spot generally providing higher discounts compared to Fargate. EC2 spot also requires you to choose the backing instance pools, whereas in Fargate, AWS takes the decision.  ECS automates spot lifecycle management by integrating with [AWS Auto Scaling](https://www.sedai.io/roles/engineering) Groups. When there is an interruption, the ASG will try to provide you with a replacement instance, from another spot instance pool, depending on your configuration. For most fault-tolerant workloads, replacing an interrupted instance is more than enough to ensure availability. ECS also supports automated spot instance draining. This can be enabled by passing a parameter to the [ECS container agent](https://www.sedai.io/) via user data of your container instance.  Once enabled, ECS will place instances in a draining state when it receives the 2 minute spot interruption notice. All the tasks running on these instances will first be sent a SIGTERM signal, and then a SIGKILL signal 30 seconds after. This lets you stop your application gracefully, or even do that last mile log collection. ECS also deregisters all such tasks from the load balancer target group, while trying to reschedule them on the remaining available instances. ‍ #### **Mixing on Demand and Spot**  ‍A mix of on-demand and spot capacity can bring in considerable savings while ensuring availability. For example: suppose you have a fleet running entirely on on-demand, which you want to overprovision by 50%. By using spot capacity to overprovision, you will only need to pay 5-10% more than your original cost.  With ECS, it is easy to [achieve dynamic capacity](https://www.sedai.io/resources/aws-ebs-datasheet) type splits. You can specify the weight of each capacity type and the backing ASG handles the rest. For example: If you provide on-demand with a weight of two, spot with a weight of three, and you have 10 instances provisioned by the ASG. Then four of those instances will be on-demand and six of them will be spot. ‍ ## **Savings Plans and Reserved Instances** Choosing Savings Plans and Reserved Instances can provide further gain. Savings Plans offer more flexible usage patterns than Reserved Instances.  Below is a comparison of effective discounts relative to on demand prices for a range of purchase options. Source: Mark Butcher via [LinkedIn](https://www.linkedin.com/pulse/aws-savings-plans-my-personal-view-mark-butcher/) ## **Overall Impacts of Purchasing Strategy** Your final implementation should take all these options into consideration while keeping in mind their potential downsides. You can have a dynamic mix of on-demand and spot capacities wherein spot interruptions have minimal impact along with optimal multi-year commitments backed by proper understanding of your workload requirements.  In a very rough scenario of a mix of on-demand, spot, Reserved Instances, and Savings Plans in equal shares at maximum discount levels, you could achieve a 50% reduction in the overall cost. ‍ | Type | Compute SP for EC2 | EC2 Convertible RI | EC2 SP | EC2 Standard RI | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 1yr | 3yr | 1yr | 3yr | 1yr | 3yr | 1yr | 3yr | | | No upfront | 21% | 45% | 21% | 45% | 31% | 52% | 31% | 52% | | Partial upfront | 24% | 49% | 25% | 49% | 34% | 55% | 34% | 55% | | All upfront | 26% | 50% | 26% | 50% | 36% | 58% | 36% | 58% | Discounts from On Demand Prices ### Frequently Asked Questions Run interruption-tolerant ECS tasks on Spot for the deepest discounts, cover steady baseline usage with Savings Plans or Reserved Instances, and keep On-Demand only for unpredictable peaks. Layering these models maximizes savings. #### How do you optimize cost with ECS Spot, Savings Plans, and Reserved Instances? Savings Plans give flexible discounts based on a committed hourly spend across instance families and regions, while Reserved Instances discount specific instance types. Savings Plans are generally more flexible for changing workloads. #### What is the difference between Savings Plans and Reserved Instances for ECS? Use Spot for stateless, fault-tolerant, or batch tasks that can handle interruptions, since Spot offers steep discounts but can be reclaimed. Avoid Spot for workloads that cannot tolerate sudden termination. #### When should I use Spot Instances with ECS? Sedai autonomously rightsizes ECS tasks and aligns capacity with demand, helping teams capture Spot, Savings Plan, and Reserved Instance value while protecting availability. #### How does Sedai optimize ECS purchasing and sizing? --- # Understanding Amazon ECS Source: https://sedai.io/blog/understanding-amazon-ecs-definitive-guide-introduction In this post we'll take a look at ECS vs other Amazon Compute models and some key concepts in Amazon ECS. ## Introduction In this post we'll take a look at ECS vs other Amazon Compute models and some key concepts in [Amazon ECS](https://www.sedai.io/platform/aws-ecs-cost-optimization). ECS is a widely used container platform; here are the numbers previously shared by Amazon about ECS at re:Invent: - Over 2.25 billion tasks are launched every week - Tens of thousands of API requests are served per second - ECS runs in 30 AWS regions, 6 continents - 65% of all new AWS containers [customers use Amazon ECS](https://www.sedai.io/resources#Customer-Stories) ## **ECS vs Other Amazon Compute Models** [Amazon Web Services (AWS)](https://www.sedai.io/video/mastering-autonomous-optimization-for-amazon-ecs) operates on a shared responsibility model, where the cloud service provider, Amazon, manages certain components of the cloud infrastructure, while users are responsible for managing other specific aspects. The degree of user involvement varies based on the compute model selected. Here, we explore four of [Amazon's compute models](https://www.sedai.io/blog/mastering-autonomous-optimization-for-amazon-ecs), ranked from the most to the least user effort required. 1. **Amazon EC2 (Elastic Compute Cloud)**Flexibility and User Responsibility: EC2 offers high flexibility, allowing users to quickly provision virtual machines (VMs) and integrate them into networks. While Amazon provides a range of features, users are responsible for:System patchingSecurity managementConnecting to appropriate load balancersPerforming similar maintenance tasksEC2 is best suited for applications that require detailed, custom configurations. 2. Flexibility and User Responsibility: EC2 offers high flexibility, allowing users to quickly provision virtual machines (VMs) and integrate them into networks. While Amazon provides a range of features, users are responsible for:System patchingSecurity managementConnecting to appropriate load balancersPerforming similar maintenance tasks 3. System patching 4. Security management 5. Connecting to appropriate load balancers 6. Performing similar maintenance tasks 7. EC2 is best suited for applications that require detailed, custom configurations. 8. **Amazon ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service)**Container Orchestration Platforms:ECS: Amazon's native container orchestration service that supports both EC2-backed clusters and serverless container clusters. It simplifies running containers at scale.EKS: Amazon's managed Kubernetes service, offering automated Kubernetes management.Users can choose to run ECS on:some textTraditional EC2 instancesServerless infrastructures via AWS FargateResponsibilities in [ECS](https%252525253A%252525252F%252525252Fhttps%252525253A%252525252F%252525252Fsedai.io%252525252Fplatform%252525252Fecs) and EKS primarily involve configuration and scaling decisions. 9. Container Orchestration Platforms:ECS: Amazon's native container orchestration service that supports both EC2-backed clusters and serverless container clusters. It simplifies running containers at scale.EKS: Amazon's managed Kubernetes service, offering automated Kubernetes management. 10. ECS: Amazon's native container orchestration service that supports both EC2-backed clusters and serverless container clusters. It simplifies running containers at scale. 11. EKS: Amazon's managed Kubernetes service, offering automated Kubernetes management. 12. Users can choose to run ECS on:some textTraditional EC2 instancesServerless infrastructures via AWS Fargate 13. Traditional EC2 instances 14. Serverless infrastructures via AWS Fargate 15. Responsibilities in ECS and EKS primarily involve configuration and scaling decisions. 16. **AWS Fargate**Serverless Container Execution: Fargate enables users to run containers without managing servers or clusters. This model abstracts the underlying server and infrastructure management, focusing user effort on:Container configurationApplication scalingFargate integrates with both ECS and EKS, simplifying deployments and management of containers in a serverless environment. 17. Serverless Container Execution: Fargate enables users to run containers without managing servers or clusters. This model abstracts the underlying server and infrastructure management, focusing user effort on:Container configurationApplication scaling 18. Container configuration 19. Application scaling 20. Fargate integrates with both ECS and EKS, simplifying deployments and management of containers in a serverless environment. 21. **AWS Lambda**Lowest Management Overhead: Lambda represents the pinnacle of AWS's serverless offerings, requiring minimal user management. It automatically handles:ScalingInfrastructure maintenancePerformance optimizationUsers are primarily responsible for code deployment and setting the function's triggers. 22. Lowest Management Overhead: Lambda represents the pinnacle of AWS's serverless offerings, requiring minimal user management. It automatically handles:ScalingInfrastructure maintenancePerformance optimization 23. Scaling 24. Infrastructure maintenance 25. Performance optimization 26. Users are primarily responsible for code deployment and setting the function's triggers. The table below covers what AWS and you are expected to manage in each of the four models: So if you look across all these compute models, they all expect development teams to still manage a number of tasks. Questions you are faced with can include:  - What version of code are you deploying? - What's your compute profile? - What's your horizontal compute profile? - How does your application react to different seasonality patterns? So there are a lot of challenges that application teams face when they manage their applications. ECS cost optimization requires more than scaling policies. [Book a demo](https://sedai.io/demo) to see how Sedai manages task rightsizing, spot usage, and idle resource cleanup in one platform. ## **The Three Layers of AWS ECS** Amazon Elastic Container Service (ECS) is designed to facilitate scalable and efficient container orchestration. Here's a brief overview of its three core layers: 1. **Provisioning Layer**Methods for Provisioning ECS Clusters: AWS offers multiple approaches including the AWS CLI, Infrastructure as Code (IAC) with AWS CloudFormation, AWS Cloud Development Kit (CDK), and Terraform. These tools cater to different development preferences and operational requirements. 2. Methods for Provisioning ECS Clusters: AWS offers multiple approaches including the AWS CLI, Infrastructure as Code (IAC) with AWS CloudFormation, AWS Cloud Development Kit (CDK), and Terraform. These tools cater to different development preferences and operational requirements. 3. **Controller Layer**Container Scheduler: Central to ECS, this fully managed scheduler handles container placement, lifecycle management, and auto-scaling, optimizing resource use and application performance. 4. Container Scheduler: Central to ECS, this fully managed scheduler handles container placement, lifecycle management, and auto-scaling, optimizing resource use and application performance. 5. **Capacity Layer**Flexible Capacity Options: ECS supports various environments:some textECS on EC2: Customizable capacity using EC2 instances.AWS Fargate: Serverless container execution without managing servers.ECS Anywhere: Run ECS on external or on-premises environments.AWS Outposts: Extend AWS infrastructure and services to any data center for a consistent hybrid experience. 6. Flexible Capacity Options: ECS supports various environments:some textECS on EC2: Customizable capacity using EC2 instances.AWS Fargate: Serverless container execution without managing servers.ECS Anywhere: Run ECS on external or on-premises environments.AWS Outposts: Extend AWS infrastructure and services to any data center for a consistent hybrid experience. 7. ECS on EC2: Customizable capacity using EC2 instances. 8. AWS Fargate: Serverless container execution without managing servers. 9. [ECS Anywhere](https://aws.amazon.com/ecs/anywhere/): Run ECS on external or on-premises environments. 10. AWS [Outposts](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ecs-on-outposts.html): Extend AWS infrastructure and services to any data center for a consistent hybrid experience. ### Amazon ECS Cost Optimization Across Task Rightsizing, Spot Usage, and Idle Resource Cleanup in One Platform See how Sedai manages the full ECS cost surface by rightsizing task definitions, optimizing Spot capacity, and reclaiming idle Fargate and EC2 resources. ## **Key Concepts in Amazon ECS** Amazon Elastic Container Service (ECS) offers a powerful solution for orchestrating containerized tasks and services. To grasp its core concepts and functionalities, let's delve into the fundamental components that make up ECS: - **Clusters** play a pivotal role in ECS. Essentially, a cluster serves as a logical grouping mechanism for tasks, services, and container instances. In the context of ECS, a cluster can represent a collection of tasks or services utilizing the EC2 launch type, or it can also denote a logical grouping of capacity providers if you leverage them. By default, ECS creates a cluster for you upon initial use, but you have the flexibility to create multiple clusters within your account to maintain resource segregation effectively. - **Container instances** are crucial building blocks within ECS. These instances refer to EC2 instances that run the ECS container agent and have been successfully registered with a specific ECS cluster. When you execute tasks using the EC2 launch type or leverage an Auto Scaling group capacity provider, ECS strategically places your tasks onto active container instances, ensuring seamless execution within your environment. - **Tasks and task definitions** form the core entities within ECS. A task represents the live instantiation of a specific task definition, running either on a container instance or within the broader AWS ecosystem. Task definitions, on the other hand, serve as blueprints for your tasks, encompassing essential details such as task names, revisions, container definitions, volume information, and more. They act as comprehensive guidelines for ECS to understand the precise configuration and requirements of your tasks. Each task can have one or more containers, such as an app container. You may have an agent or a logging agent running with it. Or you might run containers as a single container application. - **ECS services** empower you to maintain and manage a predetermined number of task instances based on your task definition. By utilizing ECS services, you can ensure a specified number of instances are consistently running within your ECS cluster. Should any task fail, the ECS service scheduler promptly launches a replacement task, guaranteeing the desired number of tasks is maintained for the service at all times. As shown below, ECS tasks are deployed as Services that can be horizontally scaled. And if it's EC2-based you deploy it on a cluster to access sufficient EC2 capacity. If it's Fargate, it's simpler - Fargate resources are used, and you don’t need to worry about the additional complications of EC2. ECS is powerful when configured correctly, but over 65% of new AWS container customers face cost drift as workloads scale. [Book a demo](https://sedai.io/demo) to see how Sedai keeps your ECS environment continuously optimized. ## **Choosing EC2 or Fargate Launch Types** When choosing a launch type for deploying applications on Amazon Elastic Container Service (ECS), it's crucial to understand the differences between the available options—EC2 and Fargate. EC2 provides granular control over your instances and is cost-effective for predictable workloads, requiring manual setup and scaling. In contrast, Fargate offers a simplified, serverless experience with automated scaling, making it ideal for unpredictable workloads and minimizing operational overhead.  Below is a detailed comparison table that lays out these differences, helping you decide which launch type aligns best with your specific application needs and operational strategy. AWS notes that while the headline costs of Fargate are more than EC2, there are less obvious costs to consider in deciding on the best model including system administration, security and compliance and scaling as shown below. ‍ ‍ | Feature | EC2 Launch Type | Fargate Launch Type | | --- | --- | --- | | Management | Requires managing instances and clusters. | Serverless; AWS manages the infrastructure. | | Control | High control over server types, configurations, and scaling. | Limited control over the computing environment. | | Cost | Cost can be lower for predictable workloads using reserved or spot instances. Charges are based on EC2 instances and EBS volumes used, billed per second with a minimum of one minute. | Generally higher per-resource cost, but only pay for resources actually used (vCPU and memory). Charges are per second with a one-minute minimum. | | Pricing Complexity | More complex with multiple instance pricing options (On-Demand, Reserved, Spot). | Simpler, based on resource usage (vCPU and memory). | | Security | More control over infrastructure to implement security measures, but requires more management overhead to keep EC2 instances secure and patched. | Workloads run in isolated environments with automatic security updates, restricted access, secure networking, and compliance with various standards. | | Setup and Scalability | More complex setup with manual scaling required. | Simplified setup; automatic scaling managed by AWS. | | Use Case Flexibility | Better for specific compute requirements and large-scale, steady-state applications. | Ideal for unpredictable workloads and applications where ease of deployment is prioritized. | | Operational Overhead | Higher, due to the need for instance management and optimization. | Lower, as there is no need to manage servers or clusters. | ### Frequently Asked Questions Amazon Elastic Container Service (ECS) is AWS's fully managed container orchestration service for running and scaling Docker containers. It supports EC2 and serverless Fargate launch types. #### What is Amazon ECS? With the EC2 launch type you manage the underlying instances and gain control and cost flexibility, while Fargate is serverless and removes server management at a premium per-task price. EC2 suits steady workloads; Fargate suits variable or low-ops needs. #### What is the difference between ECS launch types EC2 and Fargate? ECS is AWS-native and simpler to operate, while EKS runs managed Kubernetes for teams that want the Kubernetes ecosystem and portability. ECS lowers operational overhead; EKS offers more flexibility. #### How does ECS compare to EKS? Sedai autonomously rightsizes ECS tasks and services using live application signals, reducing cost while protecting performance and availability. #### How does Sedai optimize Amazon ECS? --- # High Availability for Cloud-Based Applications: Concepts & Best Practices Source: https://sedai.io/blog/basic-concepts-of-high-availability-for-cloud-based-applications Learn the basic concepts of high availability for cloud-based applications, including redundancy, monitoring, failover strategies, and best practices to minimize downtime and boost performance. Source: [AVI Networks](https://avinetworks.com/glossary/high-availability/) In a world where businesses rely heavily on digital platforms, ensuring uninterrupted access to cloud-based applications is vital. High availability (HA) is the cornerstone of achieving this goal. But what does it really mean, and how can companies implement it effectively to minimize downtime? In this article, we will discuss the critical aspects of high availability (HA) in cloud environments and discover strategies to minimize downtime and enhance performance ## **Understanding High Availability** High availability (HA) refers to the ability of a system to remain operational and accessible even when certain components fail. It is commonly measured in uptime percentages, with a focus on achieving "nines" in terms of availability. For example, **99.999% uptime**—referred to as “five nines”—translates to approximately five minutes of downtime per year. This level of uptime is crucial for cloud-based systems where even brief outages can have far-reaching consequences. Achieving this kind of availability requires strategic planning and investment. While five nines uptime is the gold standard, achieving 100% [high availability](https://www.sedai.io//blog/system-availability-calculation-definition-and-measurement) is nearly impossible due to several unavoidable challenges. In the context of HA in cloud computing, it's important to aim for uptime levels as high as possible by leveraging different strategies like redundancy and failover. ‍SLO adherence requires more than good monitoring. It requires action. [Book a demo](https://sedai.io/demo) to see how Sedai closes the loop between observability and optimization autonomously. ### **The Challenges in Maintaining High Availability** [Maintaining high availability](https://www.sedai.io/blog/system-availability-calculation-definition-and-measurement) is no small feat, as various challenges—ranging from hardware failures to unexpected traffic surges—can disrupt system uptime. Identifying and mitigating these obstacles is crucial for ensuring consistent service reliability. 1. **Hardware Failures**: Even the most advanced hardware can fail unexpectedly. Whether it’s server crashes, storage failures, or network outages, these hardware issues can lead to downtime if not mitigated properly. Cloud platforms are particularly vulnerable to such hardware challenges, making redundancy a core requirement. 2. **Software Bugs**: Software issues such as bugs or misconfigurations can also cause disruptions. Ensuring quick identification and resolution is key to maintaining availability. 3. **Surges in Traffic**: Unanticipated spikes in traffic—either from legitimate users or malicious actors like in Distributed Denial of Service (DDoS) attacks—can overwhelm your system. If your system isn't built to scale, availability can be severely impacted. 4. **Bottlenecks in Architecture**: Any hardware or software bottlenecks can cripple a system's ability to handle traffic or recover from failures quickly. These challenges highlight why mitigation strategies such as redundancy, failover mechanisms, and proactive scaling are critical in maintaining high availability. ### **The Significance of High Availability for Mission-Critical Systems** For systems that are essential to business operations—such as e-commerce platforms, financial applications, and healthcare systems—high availability is non-negotiable. When mission-critical systems experience downtime, the ripple effects can be disastrous, impacting revenue, customer trust, and regulatory compliance. For example, consider an e-commerce platform like Amazon during a major shopping event such as Black Friday. If the platform goes down even for a few minutes, it can lead to immediate loss of sales, customer frustration, and damage to the company's reputation. This downtime not only results in direct revenue loss but can also erode customer trust, leading to decreased customer retention and potential harm to the brand's image. The impact of downtime, in fact, extends beyond immediate financial loss, affecting customer satisfaction, regulatory compliance, and overall business continuity. Therefore, ensuring high availability is crucial to maintaining operational efficiency and safeguarding a company’s reputation and bottom line. ## **Building Blocks of High Availability: Ensuring Seamless Uptime in Critical Systems** In today’s interconnected world, ensuring systems remain operational 24/7 is not just a luxury—it's a necessity. High availability is the cornerstone of modern infrastructure, ensuring services stay online despite component failures. But achieving high availability isn't just about employing the latest technology; it’s about strategically implementing key building blocks that ensure resilience. However, understanding the distinction between building blocks and technical enablers can take time and effort. Building blocks are the core foundational elements like redundancy, monitoring, and failover that maintain system availability. In contrast, technical enablers are the tools or platforms, such as cloud services or automation frameworks, that support and implement these building blocks effectively. Let's break down these building blocks to clarify their role in the high-availability ecosystem. **1. Redundancy** Source: [Data Center Redundancy](https://www.techtarget.com/searchdatacenter/definition/high-availability) At the heart of high availability is redundancy. This foundational building block ensures that if one component fails, another is ready to step in seamlessly. Redundancy operates at multiple levels: - **Hardware Redundancy**: Duplicate servers, storage, or networking devices prevent a single point of failure. - **Software Redundancy**: Cloud platforms like AWS and Azure offer features that reroute traffic to backup systems if the primary system goes down. - **Data Center Redundancy**: Hosting services across multiple data centers ensures that if one goes offline, another can seamlessly take over. Redundancy strategies, such as active-active configurations (where multiple systems handle traffic simultaneously) and active-passive configurations (where backups are activated only when the primary system fails), are critical for maintaining seamless availability. **2. Monitoring and Failure Detection** Proactive monitoring is another critical building block. Monitoring tools help detect potential issues early, preventing small problems from escalating into major failures. These systems continuously track health metrics like CPU usage, memory, and disk I/O, providing real-time data that enables swift action. Cloud providers and third-party services offer a host of monitoring solutions to ensure that all components perform optimally. Should one server begin to degrade, automatic failover mechanisms ensure that a disruption is avoided before it becomes a full-blown outage.  **3. Failover Mechanisms** Failover is the safety net of high availability. When a component fails, failover mechanisms shift workloads to backup systems. These mechanisms come in two main types: - **Automatic Failover**: The system switches to backup without human intervention, ensuring minimal downtime. - **Manual Failover**: In this case, human intervention is required, which may lead to longer downtimes. Failover systems must be tested regularly to ensure they function as expected in real-world scenarios. Cloud providers like AWS, Azure, and Google Cloud offer automated failover systems that help businesses maintain uninterrupted service. By focusing on these key **building blocks**, organizations ensure high availability. The **technical enablers**—such as cloud services, monitoring tools, and automated failover solutions—are what implement these building blocks. Understanding this distinction is essential to crafting a resilient, always-on infrastructure. ## **High Availability: Technical Enablers for Cloud-Based Applications** With downtime having the potential to cost businesses millions, implementing the right technical enablers to ensure high availability is essential. Let us now discuss what they are and how they safeguard against disruptions in detail. **1. Data Backup and Recovery** One of the primary pillars of high availability is data backup and recovery. Regular, automated backups are crucial for preventing data loss during system failures. Backups not only serve as a safety net but also ensure that, in the event of an outage, data can be restored promptly to minimize downtime. When discussing data recovery processes, it’s important to note that businesses must be able to restore both data and applications swiftly to bring systems back to a working state. The speed of recovery can significantly affect a company’s ability to maintain operations. Moreover, the choice between local and cloud-based backups plays a crucial role in disaster recovery strategies. While local backups provide faster restoration times, cloud-based backups offer additional resilience by storing copies offsite. This ensures that, in the case of catastrophic hardware failure or natural disasters, businesses can recover their data from a remote location, enhancing their overall high availability posture. **2. Load Balancing** Source: [Load Balancing](https://en.wikipedia.org/wiki/Load_balancing_%28computing%29) Load balancing is another key enabler of high availability, ensuring that incoming traffic is distributed evenly across multiple servers or instances. By doing so, load balancers prevent individual servers from becoming overwhelmed and ensure optimal system performance, even during traffic surges. There are several load-balancing algorithms that businesses can implement, each tailored to specific application needs. For example, round-robin distributes traffic sequentially, while least connections direct traffic to the server handling the fewest active connections. These algorithms help avoid bottlenecks, ensuring seamless application availability across distributed systems. Using appropriate load-balancing mechanisms can enhance resilience and distribute workloads effectively. For businesses operating in multiple regions, geographically distributed load balancers are essential. By routing traffic based on proximity to data centers, these systems reduce latency and improve performance for users worldwide. This geographical distribution also ensures that if one region goes offline, traffic can be routed to another, maintaining high availability even in the face of localized failures. **3. Clustering** Clustering is a strategy that provides redundancy and failover capabilities, ensuring that if one node fails, others in the cluster can continue to handle the workload. This node-level redundancy is critical for maintaining uninterrupted service in high-availability systems. In a clustering setup, understanding the concept of cluster quorum is vital. The quorum helps maintain data consistency and prevents split-brain scenarios, where different parts of the system behave as if they are independent entities. This consistency ensures that the system continues to operate correctly, even during partial failures.  Synchronous clustering ensures real-time data replication across nodes, minimizing data loss in the event of a failure. However, this can introduce latency, making asynchronous clustering a better option for systems that prioritize performance over immediate data consistency. The trade-offs between synchronous and asynchronous clustering depend on the specific needs of the application, balancing performance against the risk of data loss. **4. Scalability and Capacity Management** As cloud systems grow, the ability to scale up (vertically) or scale out (horizontally) becomes a crucial component of high availability. Scaling up involves increasing the resources (e.g., CPU or RAM) of an existing server while scaling out involves adding more servers to handle increased demand. Both approaches help accommodate higher traffic loads, ensuring that systems remain available even as demands increase. Auto-scaling mechanisms take scalability a step further by dynamically adjusting resources based on real-time traffic and load. By automatically scaling up or down, businesses can ensure they have the necessary resources to handle traffic spikes without manual intervention. A related concept is cloud bursting, where on-premise systems temporarily extend to the cloud to handle peak demand. This approach is particularly useful for businesses with hybrid infrastructures, enabling them to leverage the cloud’s capacity during busy periods without permanently migrating all workloads. Cloud bursting offers flexibility, helping businesses manage costs while ensuring they have the resources needed to maintain high availability during critical periods. Finally, tools like container orchestration platforms (e.g., Kubernetes) help manage scalability in cloud environments by automating the deployment, scaling, and operation of containers. These platforms enable businesses to scale their applications easily, improving resource utilization and ensuring that high availability is maintained as the system grows. ## **Design Principles for High Availability in the Cloud** High availability is essential in cloud computing to ensure systems and applications remain operational with minimal downtime, even in the face of failures. Implementing high availability in the cloud requires a strategic approach, combining various design principles and technical enablers to maintain continuous access to critical services. Here's a breakdown of key design principles to consider.  **1. Eliminating Single Points of Failure** One of the fundamental principles in designing for high availability is eliminating single points of failure. A single point of failure refers to any part of a system that, if it fails, will cause the entire system to go down. These can exist at the hardware, software, or network level and are a major risk to system availability. To mitigate this, redundancy and load balancing are employed. Redundancy ensures that if one component fails, a backup is ready to take over. For example, in a cloud environment, replicating servers across multiple availability zones helps eliminate single points of failure. Similarly, load balancing distributes traffic across multiple servers or instances, ensuring that no single server becomes overwhelmed. For a more in-depth approach to designing for high availability, you can explore additional strategies in the article [design for scale and high availability](https://cloud.google.com/architecture/framework/reliability/design-scale-high-availability). **2. Implementing Load Balancing and Clustering** Load balancing is a key enabler for high availability as it prevents servers from being overloaded by distributing incoming requests evenly. Different load balancing algorithms, such as round-robin and least connections, help balance the load effectively across servers, ensuring optimal performance and availability. Learn more about how [load balancing and clustering](https://support.ptc.com/help/arbortext/r8.1.2.0/en/index.html#page/PE/peconfig/pe2004.html) improve scalability, availability, and performance in this resource. Cloud environments like AWS and Azure offer built-in load balancers that route traffic dynamically based on server health and performance. Clustering adds another layer of redundancy by grouping servers (nodes) to function as a unified system. In a cluster, if one node fails, the remaining nodes continue to handle requests without interruption. Clustering also involves a concept known as quorum, which ensures data consistency and prevents the system from becoming fragmented (split-brain scenarios) during node failures. Clustering, combined with load balancing, provides robust failover capabilities, enhancing the overall availability of cloud-based applications. **3. Data Replication for Redundancy** Source: [Oracle](https://docs.oracle.com/cd/E19424-01/820-4806/fpcqm/index.html) Data replication is a critical strategy in achieving high availability, ensuring that data is consistently copied across multiple locations. In cloud environments, data replication across multiple availability zones or regions helps protect against data loss due to regional outages or disasters. This type of redundancy ensures that if one region experiences an issue, the system can continue to operate using data replicated in another region. Replicating data across geographically distributed regions is one of the most effective ways to ensure business continuity and prevent the loss of critical information. **4. Documenting, Testing, and Validating Failover and Failback Procedures** In addition to redundancy and load balancing, documenting, testing, and validating failover and failback procedures is essential for maintaining high availability. Failover refers to the process of switching to a backup system when a failure occurs, while failback involves restoring services to the primary system once it is operational again. Detailed documentation ensures that teams can follow established procedures during failover events to minimize downtime. However, documentation alone is not enough. Regular testing of these procedures is critical to ensure they work effectively when needed. Validation of failover and failback processes helps identify potential gaps and ensures that teams are prepared for real-life failures. Moreover, continuous review and updates of these procedures are necessary to reflect any changes in the system architecture or business requirements. Regularly reviewing and testing failover processes ensures they remain relevant and effective in a constantly evolving environment. ## **Key Metrics for Measuring High Availability** Source: [Expand Your High Availability Metrics](https://us.sios.com/blog/expand-your-high-availability-metrics/) Let us now explore the key metrics that organizations use to measure high availability and are able to [calculate system availability](https://www.sedai.io//blog/system-availability-calculation-definition-and-measurement), ensuring systems remain operational and user expectations are met.  **1. Uptime** Uptime is the cornerstone metric for high availability, representing the percentage of time that a system is operational and accessible to users. It is often expressed as a percentage—commonly 99.9%, 99.99%, or 99.999%—which translates to different allowable downtimes per year. For example, a system with 99.9% uptime could be down for approximately 8.76 hours annually, while a system with 99.99% uptime only allows 52.56 minutes of downtime per year. The importance of uptime lies in its direct correlation to user experience and business continuity. Downtime can lead to financial losses, customer dissatisfaction, and brand damage, especially in industries reliant on continuous operations like e-commerce or financial services. For cloud services, such as those offered by AWS, Azure, and Google Cloud, achieving the highest uptime is critical to maintaining customer trust. **2. Service Level Agreements (SLAs)** SLAs are formal agreements between service providers and their customers, often outlining the expected uptime percentage. These agreements not only set customer expectations but also hold providers accountable. For instance, cloud providers like Amazon Web Services (AWS) may commit to a specific uptime (e.g., 99.99%) and compensate customers if they fail to meet this threshold. These guarantees incentivize providers to invest in the infrastructure, monitoring, and recovery strategies necessary to uphold these promises. **3. Recovery Time Objective (RTO) and Recovery Point Objective (RPO)** **RTO (Recovery Time Objective):** RTO defines the maximum amount of time a system can be offline before significant damage is caused to the business. In simple terms, it’s the target time within which a service must be restored after an outage. **RPO (Recovery Point Objective):** RPO, on the other hand, refers to the maximum amount of data loss measured in time that a business is willing to tolerate. It reflects the point in time to which data must be recovered after an outage. These metrics are crucial for organizations when setting disaster recovery (DR) plans. For instance, if an e-commerce platform experiences a critical failure, the RTO might be set to 30 minutes (ensuring the site is back online within that timeframe), while the RPO might be five minutes (meaning data is restored to no more than five minutes before the outage). **Setting Expectations** By defining RTO and RPO, organizations can create realistic expectations for internal teams and customers. This planning allows businesses to allocate resources effectively, ensuring the necessary infrastructure is in place to meet these recovery goals. For example, a financial institution with low RTO and RPO requirements may invest heavily in real-time data replication and automated failover systems to minimize data loss and downtime. **4. Mean Time Between Failures (MTBF) and Mean Time to Recovery (MTTR)** **MTBF (Mean Time Between Failures):** MTBF is a predictive measure that calculates the average time a system operates without failure. It helps organizations assess the reliability of their systems by indicating how often they can expect disruptions. **MTTR (Mean Time to Recovery):** MTTR, on the other hand, measures the average time required to recover from a failure. It includes detection, diagnosis, repair, and verification time. Both MTBF and MTTR are essential for understanding system resilience. A high MTBF indicates a reliable system with fewer breakdowns, while a low MTTR reflects a faster recovery process, ensuring minimal disruption to end-users. **Improving Reliability** Organizations can improve MTBF by investing in robust, redundant hardware and software solutions, such as AWS’s Elastic Load Balancing or Google Cloud’s failover systems. Automating failure detection and recovery mechanisms helps reduce MTTR, enabling systems to get back online faster. In PayPal’s case, their collaboration with Sedai helped significantly reduce MTTR by automatically detecting and correcting anomalies before they become critical issues. ### High Availability Requires More Than Redundancy and Monitoring See how Sedai helps engineering teams maintain high availability by autonomously optimizing cloud resources, preventing performance degradation, and improving application resilience in real time. **5. Failed Customer Interactions (FCI)** The [Failed Customer Interactions (FCI) ](https://www.sedai.io/blog/fcis-are-the-new-availability)metric measures a system’s capacity to maintain operations in the face of failure. While metrics like uptime and MTBF focus on availability, FCI provides insights into how effectively failures are managed without causing system downtime. FCI puts the focus on the customer experience with the application, a critical consideration that isn’t measured by other metrics. FCI tracks not just whether a failure occurred but how quickly and effectively the system reacted to mitigate it. It plays a vital role in environments where high availability isn’t only about prevention but also about rapid recovery and containment of failures. **Integration with Monitoring**FCI becomes particularly powerful when integrated with continuous monitoring tools. Combining FCI with tools like AWS CloudWatch or Azure Monitor allows organizations to gather data on system performance, detect anomalies, and proactively respond to potential failures. This helps maintain uninterrupted service and provides deeper insights into the robustness of failure management processes. **6. Importance of Procedures, Testing, and Validation** **Documenting Procedures is Not Enough**While documenting procedures for high availability is essential, it’s only the starting point. Simply having written processes does not guarantee success. Testing these procedures in real-world conditions is critical to ensure they work effectively during an actual outage. **Simulating Real-World Failures**One of the best ways to test high-availability systems is to simulate real-world failures. Organizations should regularly test their failover systems, monitoring tools, and recovery plans to verify that they meet predefined RTO and RPO goals. Companies like Netflix have implemented tools such as Chaos Monkey to simulate outages, testing the resilience of their systems in production environments. **Continuous Improvement through Testing**High availability is not a set-it-and-forget-it process. Regular testing helps uncover weak points, allowing organizations to fine-tune their procedures and infrastructure. As technologies evolve, continuous improvement through rigorous testing becomes vital to keeping systems highly available and reliable. ## **Overcoming Challenges in Cloud High Availability** Achieving and maintaining high availability comes with its own set of challenges, from architectural complexity and costs to the selection of the right technologies and ongoing system reviews. Here are the key challenges businesses face when implementing high availability, along with strategies to overcome them. **1. Choosing the Right Architecture: The Foundation of High Availability** Selecting the correct architecture at the outset lays the groundwork for achieving high availability in cloud applications. A well-thought-out architecture balances complexity and cost while integrating redundancy and failover mechanisms to ensure minimal downtime during outages. Source: [Steps to achieve High Availability Architecture](https://www.filecloud.com/blog/an-introduction-to-high-availability-architecture/) **Complexity and Cost** The architecture you choose during the initial design phase plays a foundational role in shaping the high availability potential of your cloud applications. While HA is non-negotiable for mission-critical applications, it introduces significant complexity and cost. Organizations must weigh the trade-offs early on. A simpler architecture may minimize upfront costs but often requires manual intervention during failures. In contrast, a more complex, automated HA architecture, with built-in redundancy, failover, and advanced monitoring systems, increases costs but minimizes manual effort and risk during outages. The complexity comes from having to manage multiple layers of redundancy and failover mechanisms to ensure no single point of failure. For example, data and application layers need to have failover plans to ensure that if a region goes down, another region takes over without downtime . **Balancing Cost and Complexity**Implementing high availability requires a balance between cost and complexity. The adoption of a phased approach can help organizations gradually introduce HA features as they scale. Starting with essential features such as basic redundancy and backup mechanisms allows for a lean, cost-effective entry point. As the business grows, you can incrementally introduce more sophisticated solutions such as real-time replication, load balancing, and geo-distributed data centers. Leveraging cloud-native tools that automate repetitive tasks, like AWS Auto Scaling or Google Cloud’s Managed Instance Groups, can further optimize cost while maintaining robust availability. **Adopting a pay-as-you-go model** for cloud resources can be an effective way to control costs. This approach allows businesses to invest in redundancy and failover mechanisms only as needed, thereby reducing the financial burden of high availability until they reach the scale that demands it. **2. Scaling for Growth: Ensuring HA in an Expanding Cloud Environment** As your cloud environment scales, maintaining high availability becomes increasingly complex. It requires continuous adaptation of your architecture and the implementation of scaling techniques such as load balancing and geo-replication to ensure consistent uptime as demand increases. **Scalability and Testing**As your application scales, ensuring high availability becomes more challenging. More users, more data, and more services mean additional components need to work together, increasing the risk of failure. This growth introduces new complexity, requiring organizations to evolve their initial architecture to accommodate these changes. To maintain availability, you need to implement strategies such as load balancing, geo-replication, and data redundancy across multiple regions. These strategies ensure that even as the application scales, your users continue to experience minimal downtime. For instance, AWS offers features like Elastic Load Balancing, which automatically distributes incoming traffic across multiple targets, ensuring no single resource becomes a point of failure. **The Importance of Real-World Testing**Ensuring scalability and resilience requires thorough and regular testing under realistic failure conditions. Simulating system failures is key to validating your HA setup. Tools like Netflix’s Chaos Monkey, which randomly terminates instances in production to test fault tolerance, are examples of how organizations can ensure their systems recover quickly and efficiently in real-world failure scenarios. Such stress-testing uncovers potential weak points in the system, helping you address them before they result in an actual outage. Without this proactive testing, even the best-designed systems can fail when unexpected problems arise, affecting your application's scalability and availability. **3. Selecting the Right Cloud Providers and Technologies** Choosing the right cloud provider and technologies is critical to your high availability strategy. Evaluating factors like availability zones, built-in redundancy, and the cost-performance trade-off ensures your infrastructure is resilient and tailored to your specific business needs. **Technology and Vendor Selection**Choosing the right cloud provider is a crucial decision that directly affects your high-availability strategy. Vendors like AWS, Microsoft Azure, and Google Cloud offer a variety of tools and services that help automate redundancy, failover, and backups. However, they each come with different levels of support for HA, and selecting the right vendor means understanding how well their infrastructure aligns with your specific HA needs. Here are the key considerations for vendor selection: - **Availability Zones:** One of the most important factors when choosing a cloud provider is their use of multiple availability zones. Providers like AWS and Azure allow you to deploy resources across multiple availability zones to ensure that a single data center outage doesn’t impact your service. - **Built-in Redundancy Tools:** Look for vendors that offer built-in HA solutions like automated failover mechanisms, load balancing, and data replication. For example, Azure’s Availability Sets ensure that VM instances are distributed across multiple hardware resources, reducing the risk of downtime. - **Cost vs. Performance:** Selecting the best combination of cost and performance is essential. While high-performance cloud infrastructure delivers better uptime and lower latency, it often comes at a premium. Organizations must carefully balance the costs associated with these services against the business value of enhanced availability. ‍**4. Continuous Improvement: Reviewing and Updating Your HA Strategy** High availability is an ongoing process, requiring regular reviews and updates to align with evolving technologies and business requirements. Continuously optimizing your architecture ensures it stays effective as your company and cloud environment grow. **Regular Reviews and Updates**High availability is not a one-time setup. As technologies evolve, so must your HA strategy. Regular reviews ensure that your architecture remains aligned with both business requirements and technological advances. For example, as **cloud providers** introduce new services or improve existing ones, there may be opportunities to optimize redundancy, improve failover times, or reduce costs. Continuous updates to your architecture should also reflect changes in your business’s operational environment. A growing business with increased user demand may require more advanced load balancing or failover solutions than it initially implemented. Regularly auditing your HA architecture ensures it remains capable of supporting the business as it scales. **Keeping Up with Evolving Technologies**Cloud providers frequently roll out new tools, regions, and best practices for high availability. To stay competitive, it’s essential to keep pace with these advancements. For example, AWS regularly introduces new services like AWS Outposts, which extend AWS infrastructure to on-premises environments, providing hybrid cloud solutions that can further enhance HA. Keeping up with such technologies ensures that your system architecture remains not only scalable but also future-proof. Periodic reviews and updates also include testing your failover mechanisms and disaster recovery plans. This ensures that your recovery processes and automation scripts remain aligned with the latest best practices, reducing the risk of failure during an actual outage. ## **Testing and Monitoring for High Availability** Ensuring high availability (HA) requires not only a robust system architecture but also a rigorous approach to testing and continuous monitoring. Without these practices, even the most well-designed systems can suffer unexpected failures, leading to downtime and disruptions. In this section, we’ll explore the critical importance of rigorous testing, proactive monitoring, and early warning systems to maintain high availability in cloud-based environments. **1. Rigorous Testing** Testing your HA setup under realistic failure conditions is crucial to identifying weaknesses and preparing your system for actual failures. In high-availability architectures, testing goes beyond regular unit tests or functional checks—it involves simulating failures to ensure systems can handle disruptions seamlessly. - **End-to-End Testing:** To ensure true resiliency, end-to-end testing is necessary. This type of testing evaluates every component of the system—from databases to networking and application layers—under stress conditions. This method ensures that automated failovers, backups, and redundancies work as expected under real-world conditions. - **Simulating Failures:** Simulating various failure scenarios, such as power outages, network disruptions, or database failures, helps uncover weak points in the system that would otherwise go unnoticed. By testing failover mechanisms and backups during peak traffic, businesses can ensure they are ready to maintain uptime even when critical components fail. - **Importance of Regular Testing:** Without regular testing, there’s a risk that systems will not recover as expected during a real outage. Regularly scheduled testing also verifies that the system evolves with changing demands and infrastructure updates. This process should be ingrained in operational workflows to ensure preparedness at all times. ‍**2. Proactive Monitoring** Testing alone isn't enough—continuous, proactive monitoring is equally important to maintaining high availability. Monitoring tools allow organizations to detect potential issues early and respond before they escalate into major outages. - **Health Probes and Monitoring Tools:** Proactive monitoring involves setting up health probes and tracking key system metrics, such as CPU utilization, memory usage, and disk I/O. Tools like AWS CloudWatch, Google Cloud Monitoring, and Azure Monitor provide real-time visibility into system health. These tools also automate responses to detected issues, such as scaling resources or triggering failover mechanisms, ensuring continuous availability . - **The Role of Automation in Monitoring:** Automation is key to successful monitoring in HA setups. For instance, if a certain threshold of CPU utilization is exceeded, monitoring systems can automatically reroute traffic or provision additional resources. This proactive action helps prevent issues from affecting end-users, ensuring high availability without manual intervention. ‍**3. Early Warning Systems** To take monitoring to the next level, setting up early warning systems**** with alerts based on critical system indicators is essential. These systems enable organizations to anticipate and address potential issues long before they become significant problems. - **Alerts Based on Key Indicators:** Early warning systems trigger alerts based on key indicators such as abnormal traffic spikes, increasing error rates, or rising latency. For example, an early warning system can be set up to send an alert when latency exceeds a defined threshold, signaling the need for immediate action to prevent downtime. These alerts allow for swift action, often preventing outages from ever reaching end-users . - **Proactive Issue Resolution:** By setting up alerts based on performance metrics and system health checks, IT teams can take a proactive approach to issue resolution. Instead of reacting to failures, early warning systems help businesses resolve issues before they escalate, maintaining seamless operations for users. ## **Case Study: Improving PayPal's Availability from "3 9s" to "4 9s"** Ensuring high availability is a critical mission for global financial institutions like [PayPal](https://www.usenix.org/sites/default/files/conference/protected-files/srecon18americas_slides_hummel.pdf), where even a few minutes of downtime can lead to significant financial losses and damage to customer trust. PayPal’s journey to improve its system availability from **99.9% ("3 nines")** to **99.99% ("4 nines")** offers valuable insights into the strategic decisions and technical innovations required to achieve such a level of resilience. This shift represented a dramatic improvement, reducing annual downtime from around **8.76 hours** to **52.56 minutes**. PayPal’s path to this achievement involved a combination of technical enhancements, automation, proactive monitoring, and cultural shifts within the engineering teams. ### **The Challenge: From 3 Nines to 4 Nines** At 99.9% availability, PayPal’s systems were experiencing downtime that, while relatively short, still posed a risk to user trust and could lead to lost transactions, particularly during peak usage times. The challenge for PayPal was to elevate their availability to 99.99%, a goal that required addressing not only technical issues but also cultural and procedural practices. Achieving “four nines” requires tackling complex failure scenarios that only manifest under specific, and often rare, conditions. Failures might occur due to dependencies between systems, third-party service issues, or human error during maintenance. PayPal’s objective was to create an infrastructure that could automatically detect and recover from these failures without manual intervention. ### **Key Initiatives for Achieving 4 Nines Availability** #### **1. Architectural Simplification and Dependency Isolation** One of the first steps PayPal took was to simplify its system architecture. Complex architectures with multiple interdependent services increase the likelihood of cascading failures. By breaking down services into smaller, decoupled components, PayPal could isolate potential failures to specific areas of the system, reducing the chances that an issue in one service would bring down the entire platform. Additionally, by separating critical services from less critical ones, PayPal ensured that high-importance features—such as transaction processing—were shielded from failures in non-essential systems, such as notification services. #### **2. Automation: Reducing Human Intervention** Manual interventions during incidents often lead to extended recovery times, as human errors or delayed responses can worsen an already precarious situation. PayPal moved toward an automation-first approach. This included the automation of: - **Failovers**: Automatic rerouting of traffic during failures to avoid downtime. - **Scaling**: Dynamically adjusting resources based on real-time demand to avoid bottlenecks. - **Recovery Processes**: Automatically triggering recovery mechanisms, such as rebooting services, without human intervention. By automating these processes, PayPal minimized the potential for human error and significantly reduced the mean time to recovery (MTTR). #### **3. Proactive Monitoring and Predictive Analytics** PayPal adopted a comprehensive approach to proactive monitoring, using tools like **Sedai** and machine learning to analyze system performance and predict potential failures before they occur. Monitoring wasn't limited to individual system components but included end-to-end application health, which helped PayPal detect subtle signs of degradation that might otherwise go unnoticed. Predictive analytics enabled PayPal to anticipate system stress points, such as increased traffic during peak hours or anomalies in resource utilization. This allowed them to take proactive actions—such as redistributing traffic or spinning up additional resources—before the problem escalated into an outage. #### **4. Chaos Engineering and Failure Testing** Inspired by Netflix’s Chaos Monkey approach, PayPal introduced chaos engineering to actively simulate failures in its production environment. By randomly terminating instances or disabling network connections, PayPal tested the resilience of its infrastructure under real-world failure conditions. This helped the engineering teams identify weak points in their systems that would have been hard to detect under normal testing conditions. Automated testing also played a major role in ensuring that failover mechanisms were functioning as expected, eliminating potential points of failure during traffic spikes or service disruptions. #### **5. Cultural Shifts: Building a Reliability-First Mindset** PayPal’s shift to “4 nines” availability wasn't purely technical—it involved significant cultural changes as well. The company fostered a reliability-first mindset within its engineering teams, where high availability became a shared responsibility rather than a specialized concern. This cultural shift involved: - **Blameless post-mortems**: When an incident occurred, the focus was on learning and improving, not assigning blame. - **Cross-functional collaboration**: Teams from different departments worked together to ensure that any system change or update factored in its impact on availability. This collaborative and proactive approach helped PayPal to stay ahead of potential issues and ensured that every decision, from infrastructure changes to software updates, was made with high availability in mind. ### **Results: Achieving Four Nines Availability** The results of PayPal’s efforts were clear: the company successfully increased its system availability to **99.99%**. This improvement reduced downtime from over 8 hours per year to less than an hour, drastically improving the user experience during critical times such as holiday shopping or global sales events. Key benefits included: - **Enhanced customer trust**: With near-zero downtime, customers gained greater confidence in PayPal’s services. - **Operational efficiency**: Automated failovers and predictive analytics reduced the need for manual interventions, saving both time and resources. - **Scalability**: PayPal’s systems were better equipped to handle traffic spikes and service disruptions without negatively impacting users. PayPal’s journey to “four nines” availability demonstrates that achieving high availability is not just about having the right technology—it’s about adopting the right mindset, fostering collaboration, and continuously testing your systems under real-world conditions. Key takeaways from PayPal’s experience include: - **Simplifying architecture** to isolate dependencies and reduce complexity. - **Automating critical processes** to minimize recovery times and prevent human error. - **Implementing proactive monitoring** and **predictive analytics** to detect and resolve issues before they affect users. - **Investing in failure testing** to simulate real-world conditions and uncover hidden weaknesses. - **Fostering a culture** where reliability is everyone’s responsibility, ensuring that high availability is a continuous priority. By taking these steps, businesses can achieve greater availability and ensure that their systems remain resilient even under the most challenging circumstances. High availability architecture is expensive to over-engineer and risky to under-engineer. [Book a demo](https://sedai.io/demo) to see how Sedai keeps reliability costs calibrated to your actual SLO targets. ## **Elevate Your Cloud Availability with Sedai: AI-Powered Precision for Performance and Cost Efficiency** Ensuring high availability in the cloud goes beyond implementing the right tools; it requires proactive resource management to support robust systems that prevent disruptions and optimize performance. In this fast-paced, dynamic environment, it's crucial to have a solution that can optimize resource allocation and cost efficiency while ensuring uninterrupted service. Sedai is designed to tackle these challenges head-on. By providing real-time monitoring and dynamic adjustments, Sedai optimizes resource utilization, preventing performance bottlenecks and ensuring that your systems maintain high availability. [Schedule a demo](https://www.sedai.io/demo) today to learn how Sedai can help enhance the high availability of your systems while maximizing operational efficiency and minimizing costs. Ensure your business is equipped to handle challenges with the power of AI-driven solutions. ### Frequently Asked Questions High availability (HA) is the design of systems to stay operational with minimal downtime, using redundancy, failover, load balancing, and health checks. The goal is to eliminate single points of failure. #### What are the basic concepts of high availability? Through multi-AZ or multi-region deployments, redundant instances behind load balancers, automated failover, and health monitoring that reroutes traffic away from unhealthy nodes. Cloud providers supply building blocks like availability zones and managed load balancers. #### How is high availability achieved in the cloud? High availability minimizes downtime and recovers quickly from failures, while fault tolerance aims for zero interruption by running fully redundant systems in parallel. Fault tolerance is more costly and is reserved for the most critical workloads. #### What is the difference between high availability and fault tolerance? Sedai continuously monitors application health and autonomously optimizes resources to prevent performance degradation and outages, helping teams maintain availability targets without manual effort. #### How does Sedai support high availability? --- # FinOps & Engineering Both Want to Lower BigQuery Costs. So Why Hasn't Anyone? Source: https://sedai.io/blog/finops-engineering-lower-bigquery-costs If your team is using BigQuery at scale, there's a good chance your FinOps and engineering teams are both trying to lower the bill, but neither are succeeding. This is why. If your team is using BigQuery at scale, there's a good chance your FinOps and engineering teams are both trying to lower the bill, but neither are succeeding. This is why. A customer’s data engineering team came to us with a BigQuery spend problem. They were operating at roughly **150K committed slots**, with tens of thousands in production, spread across **100 reservations** in **more than 20 regions**. Costs were climbing, SLOs were missed, and despite knowing *why*, no one could *act* on it.  Capacity was controlled by the FinOps team, which operated on budgets & contracts, not workload behavior. So, baseline slots were locked into long-term commitments, autoscale limits were configured conservatively, and any change required coordination with engineering that moved too slowly. GCP costs shift constantly across compute, storage, and data services. [Book a demo](https://sedai.io/demo) to see how Sedai keeps your Google Cloud environment continuously optimized. At the same time, the system itself was opaque, with no clear way to answer basic questions like:  - Which reservations are consistently underutilized?  - Which reservations are starved? - How are baseline capacity & autoscale actually used over time?  Even with idle slot sharing enabled, capacity remained fragmented. Some reservations sat idle while others queued. The result was predictable: Engineering saw latency spikes and FinOps saw rising costs. Neither team had the control needed to fix the problem. This is not a unique story. It is, in fact, the defining cloud cost story of the last five years. BigQuery just makes it impossible to ignore. ## Why BigQuery Is Where This Problem Gets Expensive BigQuery is great at removing infrastructure complexity. There's no cluster to provision, no node type to select, and no autoscaling policy to wire together manually. You simply write queries and jobs run; Google handles the underlying infrastructure. The cost model, though, creates a structural problem that, as we saw with our customer, neither FinOps nor engineering can solve alone. [BigQuery charges by slot usage, not by infrastructure.](https://sedai.io/platform/bigquery) A slot is Google's abstracted unit of compute, which combines CPU, memory, & I/O into a single billable resource.  Traditionally, you can set up your slots like this: - **Buy a baseline** amount upfront at a committed rate - **Set an autoscale maximum** for demand spikes at a higher pay-as-you-go rate and  - **Distribute those slots** across reservations scoped to your teams & workloads This configuration determines your bill & your query performance simultaneously. However, between baseline slots, reservations, & autoscaling slots, there’s potential for a staggering amount of BigQuery waste.  For instance, because teams purchase a fixed amount of slots for a fixed amount of time at a specific rate, teams still risk wasting paid capacity because their workloads don’t consistently use it.  When setting an autoscale maximum, if it’s set too high after a one-time spike, and nobody manually lowers the ceiling after, every time the autoscale kicks in for a demand increase, BigQuery scales up to that ceiling.  Because you’re billed for however many slots BigQuery spins up, you’re paying for more than your workload actually needs.  The 60-second minimum billing window makes this worse: even a short burst gets billed for a full minute at whatever scale BigQuery reached. For batch & interactive workloads that share the same reservation, BigQuery doesn't inherently know which is more urgent. When a batch job runs (like a large overnight ETL), it consumes a chunk of the reservation’s capacity. So if an analyst runs a query or a dashboard refreshes during the batch job, it must wait in the queue for slots to free up. The cost here isn’t obvious at first. Because you’re paying for baseline slots either way, the batch job consuming them doesn’t change your committed spend, it just makes it look like you have a capacity problem. The typical response is to buy more baseline slots, which only increases your spend and leaves the issue festering. BigQuery’s sneaky ways of inflating costs is then exacerbated by a bigger problem: the disconnect between FinOps & engineering. ## The Slot Problem Is Really a Coordination Problem The coordination problem between FinOps & engineering has a price tag. [According to Harness](https://www.prnewswire.com/news-releases/44-5-billion-in-infrastructure-cloud-waste-projected-for-2025-due-to-finops-and-developer-disconnect-finds-finops-in-focus-report-from-harness-302385580.html), 52% of engineering leaders said this misalignment was the leading cause of cloud infrastructure costs, which is projected to reach $44.5 billion this year.  We’ve seen this with our own customers working in BigQuery: When an engineering team notices their services that depend on BigQuery are slowing down or timing out, they escalate to a platform team. The platform team then looks at the reservation and sees high autoscale spillover. They follow up by requesting more reservations.  FinOps then reviews the request against org-level commitments & existing reservation allocations. Increasing baseline slots for one reservation is not just a local change, it also requires either: - **Reallocating capacity from another reservation** — which risks impacting a different workload - **Increasing overall commitments** — which carries long-term cost implications Without clear visibility into which reservations are consistently underutilized and which are genuinely capacity-constrained, these optimization decisions become inherently conservative. And while idle slot sharing helps absorb short term imbalance, it does not provide a reliable signal for structural reallocation. As a result, FinOps typically approves incremental autoscale increases to handle immediate demand while delaying or minimizing baseline changes. Over time, more workloads shift into autoscale, while baseline capacity remains static, leading to higher costs & continued inefficiency. And this isn’t just a BigQuery issue; according to the same Harness’ report, enterprises take an average of 31 days to identify & eliminate cloud waste, and 25 days to detect & rightsize over-provisioned resources.  The industry’s answer is to automate optimizations. But automation has become its own problem. ## The $44.5 Billion Organizational Problem That’s Now an Automation Problem For years, ["empowering engineers to take action"](https://www.finops.org/wg/encouraging-engineers-to-take-action/) was the number one challenge cited by 40% of FinOps practitioners.  But in the [State of FinOps 2026 report](https://data.finops.org/), “empowering engineers to take action,” was replaced with an industry that is looking to “shift left,” or create environments where engineers can take action proactively rather than reactively to get cloud costs under control.  It makes sense that we’re at this point. As teams become leaner and have a plethora of automated cost optimization tools to choose from, AI cost management is now the #1 desired skillset for FinOps.  Instead of the back-and-forth between FinOps & engineering, orgs can just automate the problem away.  But automation doesn't deeply know your workloads enough to always make the right optimization. It can only apply rules and react to thresholds, optimizing in isolation away from real-world behavior. ### Why Automation Makes BigQuery Costs Worse Most automation tools operate on static rules or point-in-time signals. For instance, [if we’re optimizing BigQuery](https://sedai.io/blog/bigquery-optimization), they can look at recent utilization and suggest: - Baseline slot reductions - Autoscale limit increases - Reservation rebalancing But these decisions are not static, and require context to be effective at managing waste. When we talk about reservation sizing, the right action depends on: - The time of day demand patterns - The workload mix, batch vs. interactive - Latency expectations tied to SLOs - Cross reservation dependencies under shared capacity For example, if we reduce baseline slots based on low utilization during off peak hours, it can increase autoscale dependency during peak windows. Or, if we increase autoscale limits to fix queueing, it can mask a deeper imbalance in how capacity is distributed across reservations. Automation without real-world context is inherently unstable. BigQuery makes this especially visible. A reservation change intended to reduce cost can introduce queueing for latency sensitive workloads. A conservative baseline meant to control spend can push more usage into autoscale, increasing total cost. The same automation issues that impact cost also impact performance. Reduce baseline slots too aggressively and you breach SLOs. Cap autoscale too tightly and queries get stuck in the queue. ### Ready to optimize your BigQuery Slots? Book a Sedai demo to speak with a technical expert. ## Why Autonomous Optimization is the Only Safe Optimization If FinOps & engineering teams are shifting to a more proactive approach to cutting cloud costs, they must understand automation will only break in the end, still leaving them with high costs & more toil. The only truly proactive approach to safe optimization is grounding it in real application behavior that can change as your workloads change. For BigQuery, that means the system needs to observe not just costs but: - Which workloads need them and when  - What the latency patterns look like across reservations - Where the true sweet spot sits between execution time and slot usage  In addition, humans *must* stay in the loop to review recommendations, approve changes, & retain control over the structural decisions like org-level commitment purchases. For our customers, we follow this model for BigQuery:  - Continuous observation of slot consumption patterns across workloads - SLO-aware recommendations that account for performance, not just cost  - A Copilot model where engineers & FinOps can review data-backed recommendations with projected impact before any change is made We don’t see this as removing either FinOps or engineers from the process or elevating one team’s importance over the other. Instead, we see this as providing a centralized, trusted system that can consider both teams’ perspectives and bolster that with real-world app behavior. ## The Practical Takeaway for Engineering Leaders If you're a VP of Engineering or CTO with significant BigQuery spend, you already know that your FinOps & engineering teams have a disconnect. We’re not trying to convince you otherwise. What we want you to consider is examining your own environment and asking yourself some uncomfortable questions. **First, how often do your slot reservations actually get adjusted?**  If the answer is weekly or less, you're operating on a lag that's costing you real money, because demand doesn't change on a weekly admin cycle. **Second, who decides the autoscale maximum, and do they have visibility into the SLO implications of capping it too low?**  If that decision is owned entirely by FinOps without direct input from the engineers who own your latency-sensitive workloads, you're likely either over-capped or under-capped at any given time. **Third, what happens when a workload's behavior changes?**  If the answer involves a ticket, a meeting, or a manual admin step, the lag between observation and action is measured in days — not in the minutes that actually matter when query latency starts climbing. Everyone talks about not wanting another dashboard; it’s practically cringe-worthy to suggest it. And regardless of how many engineers you add to your team, optimizations will never scale to what you need. The $44.5 billion in annual cloud cost waste is proof that more dashboards & headcount are not effective. BigQuery slot reservations provide predictable costs for high-volume workloads — but only when slot allocation stays aligned with actual job concurrency patterns. [Book a demo](https://sedai.io/demo) to see how Sedai continuously optimizes your BigQuery spend. BigQuery is where this problem becomes impossible to ignore: the slot model, reservation complexity, disconnect between cost and performance, and the lag between observation & action all converge into a spending problem that dashboards & headcount can't fix.  The teams that get ahead of this won't do so by adding more processes or more automation. They'll do it by grounding every optimization decision in how their workloads actually behave. And that's what Sedai is built to do. --- *Sedai's BigQuery optimization is available now. [Stop managing slots by hand; schedule a demo to see how.](https://sedai.io/demo) --- # Using Scheduled Shutdowns to Optimize Amazon ECS Development Costs Source: https://sedai.io/blog/amazon-ecs-scheduled-shutdowns-to-optimize-development-costs Keeping Amazon ECS development & testing environments workloads running when they're not actively used can lead to considerable expenses for teams.Implementing scheduled shutdowns of Amazon ECS cloud resources when not in use can lead to cloud cost savings with relatively little implementation effort.Four ways to achieve shutdowns for Amazon ECS include (1) manual shutdowns, (2) Lambda functions, (3) Cloudwatch templates and (4) schedules inside a platform such as Sedai. ## **Summary** - Keeping Amazon ECS development & testing environments workloads running when they're not actively used can lead to considerable expenses for teams. - Implementing [scheduled shutdowns ](https://www.sedai.io/blog/7-strategies-to-optimize-ecs-costs)of Amazon ECS cloud resources when not in use can lead to cloud cost savings with relatively little implementation effort. - Four ways to achieve shutdowns for Amazon ECS include (1) manual shutdowns, (2) [Lambda functions](https://www.sedai.io/capabilities/autonomous-optimization), (3) [Cloudwatch ](https://www.sedai.io/cloudvisionaries)templates and (4) schedules inside a platform such as Sedai. ## **Introduction** Engineering teams are looking for ways to be more [cost-efficient](https://www.sedai.io/blog/cloud-cost-management-and-optimization-best-practices), especially with the growth of the FinOps function. We project that the majority of ECS users can realize a [40% cost saving](https://www.sedai.io/platform/aws-ecs-cost-optimization) for ECS services overall by applying multiple engineering and financial optimizations including the scheduled shutdown tactic discussed below.  [Development environments](https://www.sedai.io/platform/aws-ecs-cost-optimization) can make up a significant portion of overall cloud expenses, particularly during extensive testing processes, and are a candidate for optimization. Teams using Amazon [ECS](https://sedai.io/platform/ecs) typically run some ECS services in development environments in order to develop or test planned releases. Teams may run a small number of tasks on a continuous basis for functionality testing and there is a risk these environments are left running. Other types of testing that involve high resource intensity, such as performance, security, resilience and recovery, and volume testing, could also impose significant costs. Scheduled shutdowns automatically turn off resources during periods of inactivity, typically nights, weekends, or holidays. The benefits of scheduled shutdowns are: - Cost Reduction: Turning off resources when not in use can lead to significant savings. - Security: Decreases the attack surface by minimizing the time resources are exposed to potential threats. - Environmental Benefits: Less energy consumption translates to reduced carbon emissions. We’ll then cover four methods to implement scheduled shutdowns. I would also like to thank my colleagues Benji Thomas and S. Meenakshi for their technical input. ECS cost optimization requires more than scaling policies. [Book a demo](https://sedai.io/demo) to see how Sedai manages task rightsizing, spot usage, and idle resource cleanup in one platform. ## **Run when Developers are active**‍ Developers typically work 5 days a week. According to Stack Overflow’s [Developer Survey](https://insights.stackoverflow.com/survey/2019), three quarters work less than 45 hours per week. Below is a heat map of developer working hours across multiple weeks at Ximes, a software company. In this company, while a 5 day working pattern is standard, there are some late nights, occasional Saturday working time and public holidays. Some adjustments to scheduled shutdowns to support variability in working hours will be needed. ## **Four Options to Achieve Scheduled Shutdowns in Amazon ECS** Let’s review four ways to achieve scheduled shutdowns: 1. Manual Shutdowns 2. Cloudwatch Events with Lambda Functions 3. CloudFormation or Terraform Templates 4. Sedai Schedules for Amazon ECS ## **1. Manual Shutdowns for Amazon ECS** Manual intervention involves developers and administrators manually starting and stopping non-essential services as needed. This method is the most straightforward and requires no additional setup or automation. Developers may forget to turn off resources when they are not being used, which can lead to extra costs." Advantages: - Low Effort to Implement: Requires no setup beyond existing processes. - Flexibility allows you to start or stop at any time, providing immediate control over resources. - No Costs: Involves no additional costs for automation tools or services. ## **2. Scheduled Shutdowns for Amazon ECS with Cloudwatch and Lambda** Lambda-based solutions can automate the starting or stopping of ECS services based on custom schedules (e.g., weekday working hours) or events, offering flexibility in resource management. Advantages: - Customization: Tailors the control of Amazon ECS resources based on specific operational needs. - Cost-Effective: Optimizes costs by adjusting resources in response to actual demand. - Integration: Works well with ECS and other AWS services for comprehensive management. Creating a scheduled startup and shutdown for an Amazon ECS cluster involves setting up a Lambda function to either start or stop the cluster and scheduling that Lambda function to run at specific times using Amazon CloudWatch Events. Below is an outline of the steps required and the code to implement this: ### **Step 1: Create the Lambda Function** Below is a Python script you can use in your Lambda function. This script assumes you have an Amazon ECS cluster and service you want to manage. It checks whether the event it receives is for a startup or a shutdown and sets the desired count accordingly.`‍`‍ Replace 'your\_cluster\_name' and 'your\_service\_name' with the actual names of your Amazon ECS cluster and service. Ensure your Lambda function has the necessary IAM permissions to modify Amazon ECS services. Note that setting the desiredCount to 0 will stop all running tasks, but it does not manage the underlying EC2 instances themselves. So we need EC2 scale in scale out parameters managed explicitly.  If the EC2 was provisioned manually then we need to stop the instance for cost savings to occur.  You will also still be charged for the  associated attached EBS cost. Scheduled ECS shutdowns can cut development environment costs by 60% but Lambda orchestration and desiredCount management still require careful configuration. [Book a demo](https://sedai.io/demo) to see how Sedai manages ECS cost reduction continuously beyond shutdown schedules. ### **Step 2: Schedule the Lambda Function Using CloudWatch Events** For Startup: - Open the Amazon CloudWatch console. - In the navigation pane, choose Events > Rules. - Choose Create rule. - For Event Source, choose Schedule and use the cron expression 0 8 ? \* MON-FRI \* to trigger at 8 AM, Monday through Friday. - For Targets, choose Add target, then select your Lambda function. - Configure input as Constant (JSON text) and provide {"action": "start"}. - Give your rule a name and description. Ensure the state is Enabled. - Choose Create rule. For Shutdown: - Repeat the steps for the startup schedule, but change the cron expression to 0 19 ? \* MON-FRI \* to trigger at 7 PM, and use the JSON input {"action": "stop"}. This setup ensures your ECS service starts at 8 AM and stops at 7 PM on weekdays, managing resources efficiently and cutting unnecessary costs.  Note that Amazon CloudWatch uses UTC time for all schedules, so you'll need to adjust to your local timezone and factor in daylight saving time. ## **3. Scheduled Shutdowns for Amazon ECS with CloudFormation Templates** We can also implement an Infrastructure as Code approach to manage the provisioning, scaling, and teardown of Amazon ECS resources, enabling dynamic environment management.   We can convert the steps needed to configure Lambda functions and CloudWatch events into CloudFormation templates. Advantages: - Automation: Streamlines the creation and destruction of resources. - Consistency: Ensures environments are consistently deployed and managed. - Scalability: Facilitates easy scaling and version control of Amazon ECS configurations.‍ ### Scheduled Shutdown Strategies That Reduce Amazon ECS Dev/Test Costs See how Sedai uses intelligent scheduling and automated start-stop controls for Amazon ECS to eliminate idle development resources, reduce unnecessary compute and storage costs, and improve overall cloud efficiency without manual intervention. ## **4. Sedai Scheduled Shutdown for Amazon ECS Clusters** With [Sedai](http://docs.google.com/sedai.io/signup), you can set specific schedules for when services should be shut down and restarted, with little effort helping to reduce costs and improve overall efficiency. Advantages: - Automated Cost Savings: Reduces cloud costs by scaling services according to demand. - Ease of Use: Provides a simple approach to managing scheduled shutdowns. Below is an example of a Sedai schedule to run Amazon ECS clusters at the account level from 8am-6pm five days a week in Sedai. To define a scale-down schedule inside Sedai, follow these steps inside your [Sedai account](http://docs.google.com/sedai.io/signup): 1. Navigate to **Settings > Schedules** 2. Under the Scale-down tab, click **Create Schedule** 3. Give the schedule a name 4. Select time zone 5. Choose a start time, duration, and the days when the schedule should run 6. Select which clusters the schedules should apply to. (**Note**: you can include multiple clusters in one schedule) By following these simple steps within your Sedai account, you can easily set up a schedule to run your ECS clusters only during specific hours and days, helping you to optimize your cloud costs. This automated cost savings feature is just one of the many benefits of using Sedai for managing your container lifecycles. ## **Conclusion** In conclusion, scheduled shutdowns in Amazon ECS development environments are an effective strategy for [optimizing cloud expenses](https://www.sedai.io/use-cases/cloud-cost-optimization) as well as enhancing security and supporting environmental sustainability. Whether through manual interventions, which rely on user discipline, automated Lambda functions, CloudFormation templates for scalable infrastructure management, or Sedai's user-friendly scheduling tools, each method offers unique benefits. We hope that you find one of these solutions beneficial in terms of Amazon ECS cost savings in your development environment. If you have any comments or suggestions about the solution, don't hesitate to reach out to us. ### Frequently Asked Questions Scheduling ECS dev and test environments to stop outside working hours removes billing for idle capacity, which often runs 24/7 unnecessarily. This can sharply cut non-production spend. #### How do scheduled shutdowns reduce ECS development costs? Yes, for non-production workloads that do not need to run overnight or on weekends. Automated start and stop schedules restore environments before teams need them. #### Is it safe to shut down ECS development environments automatically? Savings depend on usage, but environments idle nights and weekends can avoid paying for roughly two-thirds of the week, making this one of the simplest cost wins. #### How much can scheduled shutdowns save? Sedai autonomously optimizes ECS resource usage against real demand, eliminating idle waste while keeping production performance and availability intact. #### How does Sedai automate ECS cost savings? --- # Why Your Well-Governed Infrastructure is Still Wasting 30% of Your Cloud Budget (And How We’re Working with Spectro Cloud to Fix It) Source: https://sedai.io/blog/sedai-spectro-cloud-partnership Sedai and Spectro Cloud are the ultimate combination for Kubernetes optimization and management. At our KubeCon Europe after-party in Amsterdam this past March, someone walked up to the Sedai and Spectro Cloud crew — talking side by side — and asked the question we've been hearing for months: "Wait, don't you two kind of do the same thing?" *(Yes, it was hard to hear the question over the music)* We looked at each other and laughed. If you squint at a product page, sure, you could talk yourself into seeing overlap. Both companies work with Kubernetes. Both care about cost efficiency. Both say "optimization" a lot. The truth is we solve different problems, at different layers of the stack. And the disconnect between those layers is where many enterprises (possibly yours too) are hemorrhaging money. Spectro and Sedai have been collaborating for several months now — on projects with joint customers, on our technical integration… and yes, on the occasional bar tab in Amsterdam. The more we work together, the more obvious it becomes that these two products *belong* side by side.  [Kubernetes](https://sedai.io/platform/kubernetes) management gets easier when optimization runs itself. [Book a demo](https://sedai.io/demo) to see how Sedai reduces toil across your clusters without adding operational overhead. So, we figured it’s time to write it down. ## The Day 2 problem that dashboards don’t solve A common scenario: your platform team spends months getting their Kubernetes house in order. They standardize clusters, lock down security policies, automate provisioning, build a golden path for developers. Day 0 and Day 1 are taken care of, and the infrastructure is consistent, compliant, repeatable. Phew. But within a few months of workloads running, your cloud bills are climbing. Why? In a tale as old as time, developers have padded their resource requests with generous safety margins (“just give me 4Gi of memory, I don't want to get paged at 2am”). Autoscalers don’t help: they were tuned to defaults that made sense during initial deployment but haven't been revisited since. Same for memory limits, set based on load testing that doesn't reflect actual production traffic. Nobody's doing anything wrong in this story, exactly (we love a blameless retro, right?). It’s just that workloads and their underlying infrastructure need continuous, real-time tuning, and no human team can keep up with that across hundreds of services and dozens of clusters. The governance is solid. What's missing is the *optimization*. Most enterprises try to close this gap with visibility tools: o11y dashboards, recommendation engines, FinOps reports. These are useful, sure! But they create a backlog, not a solution. Someone still has to read the recommendation, validate it, write the PR, get it reviewed, and apply it. Multiply that by every service in every cluster, and you've described a full-time job nobody was hired to do… not SREs, not DevOps, and certainly not the app developer who owns the workload. The result is that cloud costs keep rising, putting platform teams in reactive mode. All that governance work from Day 0 and Day 1 is eroded — **because nothing is actively protecting it on Day 2.** ## Two layers, one stack The more we've worked together, the clearer this painful picture has become. And the solution is just as clear. Spectro Cloud and Sedai sit at two distinct layers of the Kubernetes stack, and both layers need to work well for enterprises to get the outcomes they're paying for. **Spectro Cloud Palette** owns the infrastructure lifecycle. Our [Cluster Profiles](https://docs.spectrocloud.com/profiles/cluster-profiles/) give platform teams a declarative blueprint for everything from the OS and Kubernetes distribution to the CNI, CSI, and add-on layers. Palette deploys and manages that full stack across cloud, data center, and edge, with powerful drift remediation, built-in security and FIPS compliance if you need it. You can think of it as the governance foundation. **Sedai** owns configuration optimization after the workloads get deployed, at Day 2 and beyond. Using [patented ML models](http://sedai.io/platform), Sedai continuously analyzes how each service actually behaves in production (CPU usage patterns, memory consumption, response latency, traffic patterns and dependencies). The platform then **autonomously right-sizes resources,** in real time. Not recommendations you'll maybe get around to next sprint — actual changes, executed safely, with automatic rollback if something moves outside acceptable bounds. One builds and maintains the road. The other keeps every vehicle on it running at peak efficiency. You need both, and critically, each one makes the other better. ## Why governance enables autonomy (not the other way around) Autonomous optimization sounds great in a pitch deck, but it makes platform teams nervous for good reason. If your clusters aren't consistently configured, automated changes can have unpredictable side effects. Mixed Kubernetes distributions, different OS versions, inconsistent security policies: all of that creates a moving target for any optimization engine trying to learn your workload patterns. That's where Palette's governance becomes Sedai's secret weapon. When every cluster is deployed from the same standardized Cluster Profile, Sedai operates on a stable, predictable surface. It's able to learn faster from this solid foundation, which ultimately means it can deliver impact faster, across a wide range of environments. Guardrails don't constrain autonomy; they make it safe. From the Sedai side, this is a meaningful difference. Customers running Sedai on well-governed clusters see stronger optimization results and fewer edge cases than those running across a patchwork of hand-rolled configurations. The more uniform the canvas, the more precisely Sedai can tune what's running on it. From the Spectro Cloud side, Sedai solves the question Palette customers inevitably ask three to six months after go-live: "Our infrastructure is standardized and secure, but our cloud bill is still climbing. Now what?" That "now what" is exactly where Sedai picks up. ## How the integration works today The [Sedai Smart Agent](https://docs.spectrocloud.com/integrations/packs/?pack=sedai-smart-agent) is available as a pack in the Palette Community Registry. Add it to a Cluster Profile, deploy, and Sedai connects to your cluster through standard Kubernetes APIs. No custom plumbing, no separate installation: the same pack-based model Palette customers already use for everything else in their stack. Once installed, Sedai typically needs two to four weeks to learn your workload patterns before optimizations reach full effectiveness. During that ramp-up, you choose your comfort level across three modes: Datapilot (recommendations only), Copilot (review and approve each change), and Autopilot (full autonomy). Most customers start in Copilot, build trust watching Sedai make the right calls, then graduate to Autopilot. Trust is earned, not assumed. For Helm-based add-on layers in Palette, the integration works cleanly today. Palette installs the pack, Sedai handles workload-level optimization, and the two systems stay out of each other's way. We're also working on deeper integration patterns for tighter coordination between Palette's desired-state model and Sedai's runtime optimizations. More on that soon. ## What this looks like for real teams A few scenarios where the combination clicks. **The FinOps team drowning in recommendations.** You've got Kubecost or your cloud provider's cost explorer showing waste everywhere, but nobody has bandwidth to action the findings. Palette governs the environment; Sedai eliminates the waste autonomously, continuously. Customers typically see a 30–40% reduction in over-provisioned resources, without anyone needing to touch a YAML file. **The enterprise that nailed the migration but lost the plot on Day 2.** The Kubernetes transformation went well six months ago. But efficiency has eroded since. Resource requests are stale, HPAs haven't been revisited, and nobody's actively optimizing because everyone moved on to the next project. Palette keeps the infrastructure governed; Sedai keeps it efficient — continuously, not just at migration time. **The team that can't hire fast enough.** Every new cluster or cloud region creates a linear need for more SREs to watch dashboards and tune configurations. Palette extends the Golden Path to new environments without proportional headcount growth. Sedai manages resource efficiency at every node. Together, they let you scale infrastructure without scaling your ops team at the same rate. ### Optimize K8s with Sedai Ready to start saving on your Kubernetes costs? Our team of K8s experts can help. ## Answering the overlap question, for good We get why people ask. The cloud native ecosystem is [crowded](https://landscape.cncf.io/), product positioning blurs together, and when two companies are standing next to each other at a party, it's natural to wonder. The short answer: Spectro Cloud doesn't optimize your workloads, and Sedai doesn't provision or govern your infrastructure. Different problems, different technology, better together. Combining cluster management with autonomous optimization ensures cost efficiency is built into the cluster lifecycle from provisioning through ongoing operation.[ Book a demo](https://sedai.io/demo) to see how the Sedai and Spectro Cloud partnership delivers that for your Kubernetes environment. ## What's next? We're deepening our mutual technical integration, and building out the playbook for customers who want to move from well-governed infrastructure to self-optimizing infrastructure. The Sedai Smart Agent pack in Palette is just the starting point. If you're running Palette today and wondering what happens after Day 1, we should talk. If you're using Sedai and want a more consistent foundation to optimize against, same. And if you're stuck in the cycle of over-provisioning, manual tuning, and cloud bills that never seem to go down... that's exactly the problem we built this partnership to solve. Reach out to your [Spectro Cloud](https://www.spectrocloud.com/get-started) or [Sedai](https://sedai.io/demo?utm_source=spectro-cloud&utm_medium=blog&utm_campaign=partnership) account team, or get in touch with either of us directly, or your preferred system integrator [partner](https://www.spectrocloud.com/partners). We're always up for a conversation about what the full cloud lifecycle looks like when governance and optimization work as one. --- # How Engineers Save on AWS EMR Costs: 2026 Guide Source: https://sedai.io/blog/how-to-optimize-for-cost-in-emr See what drives AWS EMR costs and apply key strategies like right-sizing, spot nodes and auto-scaling to slash your cluster spend. The cloud has transformed IT infrastructure, offering unmatched scalability, flexibility, and performance, but without proper oversight, costs can spiral out of control. Wasted resources, inefficient workloads, and surprise bills create financial strain, making optimization essential. The right tools provide full cost visibility, automate savings, and fine-tune cloud resources for peak efficiency. But with countless solutions available, how do you choose? Before diving deeper, if you're using AWS EMR, you can also read our companion guide:[**Amazon EMR Cost Optimization: Key Strategies for 2025,** ](https://www.sedai.io/blog/how-to-optimize-amazon-emr-costs-in-2025)where we break down how to reduce big data processing costs without sacrificing performance. Now, let’s explore how [cloud optimization tools](https://sedai.io/solution/cloud-cost-optimization) can help you take control of spending across your entire cloud environment. ## **Overview of AWS EMR Cluster Costs** Source Link: [AWS EMR: Working, Features & Use Cases](https://k21academy.com/amazon-web-services/amazon-emr/)  AWS EMR (Elastic MapReduce) — a powerful managed service for running big data workloads − e.g., Hadoop, Spark, Presto on a massive scale. AWS EMR is meant to ease the configuration and management of big data clusters, but the pricing model can be complex. Understanding the key components that contribute to the overall cost is essential for controlling and optimizing your AWS EMR expenses. ### **Cost Components of AWS EMR** When analyzing AWS EMR costs, it's crucial to break down the main components that drive up expenses: - **EC2 Instances**: AWS EMR clusters are powered by EC2 instances, and the cost of these instances is typically the largest portion of your bill. The cost depends on the instance type, size, and the duration for which the instance runs. - **EMR Services**: AWS charges an additional fee for the EMR service itself, which is calculated on a per-second basis. This fee covers the cost of running and managing the EMR cluster, including services like YARN, HDFS, and the cluster management overhead. - **S3 Storage**: Data storage is another significant contributor to EMR costs. AWS S3 is often used for storing input and output data, and charges are based on the amount of storage used, the storage class selected, and the number of operations performed (such as PUT, GET, or DELETE). - **EBS Volumes**: For temporary storage, AWS attaches EBS volumes to EC2 instances. The cost is based on the provisioned storage (measured in GiB) and the throughput. ### **Understanding the EMR Pricing Model and Billing** AWS EMR pricing operates on a pay-as-you-go model, which means you pay for what you use, without any upfront commitments. The key factors influencing how you’re billed include: - **Hourly Billing**: Most EC2 instances in EMR clusters are billed per hour. Depending on the instance size and the region, the costs can vary significantly. - **On-Demand vs. Spot Instances**: On-demand instances allow you to pay only for the compute time you use, while spot instances let you bid on unused capacity, which can result in substantial savings but with the risk of interruptions. - **S3 and EBS Storage Costs**: S3 costs are calculated based on storage usage, the frequency of data access, and the region in which data is stored. Similarly, EBS storage costs depend on the volume size and the I/O operations associated with the cluster. AWS costs are manageable when someone is watching them around the clock. [Book a demo](https://sedai.io/demo) to see how Sedai acts as that continuous layer of optimization across your AWS environment. ### **Factors Influencing the Overall Cost** Several factors influence the overall cost of an AWS EMR cluster, including: - **Cluster Size and Scaling**: The number and type of EC2 instances you choose, along with the managed scaling settings, can drastically impact costs. Over-provisioned clusters can result in wasted resources, while under-provisioning may affect performance and delay job execution. - **Data Transfer and Network I/O**: Moving data between EC2 instances, S3, and other AWS services can incur additional costs, especially if data is transferred across regions or involves high data throughput. - **Job Type and Frequency**: The nature of the workloads (e.g., batch processing vs. real-time analytics) and the frequency of job runs play a significant role in cost management. Jobs with high resource demands or frequent executions increase the overall cost. Also Read: [Understanding AWS EKS Kubernetes Pricing and Costs](https://www.sedai.io/blog/understanding-aws-eks-kubernetes-pricing-and-costs)  ## **Optimizing Resource Management** Source Link: [Getting started with Amazon EMR](https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-gs.html)  Resource management is a key area where organizations can reduce their AWS EMR costs significantly. By ensuring that the right amount of resources are provisioned and scaled dynamically, you can avoid over-provisioning and under-utilization, both of which can lead to unnecessary costs. ### **Starting with Minimal Configuration and Scaling as Needed** One of the most effective strategies for optimizing AWS EMR costs is to start with a minimal cluster configuration and scale as needed. By avoiding over-provisioning from the outset, you ensure that you aren’t paying for more resources than necessary. - **Initial Cluster Setup**: Begin with a smaller number of nodes and lower instance sizes. This allows you to scale up resources only when needed, ensuring you don’t incur unnecessary costs during periods of low workload demand. - **Dynamic Scaling**: Monitor workload patterns and adjust the cluster configuration based on job execution demands. Many organizations, particularly those with batch workloads, can achieve substantial cost reductions by scaling their clusters based on real-time needs rather than maintaining a large, always-on cluster. ### **Utilizing EMR's Resize Functionality and Monitoring Resource Utilization** AWS EMR provides an option to resize clusters based on current workload demands. This allows you to adjust the number of EC2 instances as needed without restarting the cluster or re-deploying jobs. The resize functionality ensures that resources are not wasted during idle periods while still maintaining sufficient capacity during peak times. - **Real-Time Monitoring**: Constantly monitor metrics like YARN memory utilization and HDFS capacity to determine whether your resources are being fully utilized. AWS CloudWatch can be set up to track these metrics in real time, allowing for quick adjustments to prevent inefficiency. - **Optimize Cluster Sizing**: As your workloads evolve, resize your EMR cluster based on resource utilization trends. For example, if the jobs consistently use less memory than provisioned, scale down the cluster to reduce costs, as was highlighted in Adevinta's experience with dynamic memory scaling. ### **Implementing Automatic Cluster Resizing Using EMR Managed Scaling** EMR Managed Scaling is an automated feature that adjusts the number of nodes in your cluster based on workload demand. By enabling EMR Managed Scaling, your cluster automatically scales up during high-demand periods and scales down during idle times. This helps optimize resource utilization without requiring constant manual intervention. - **Automatic Scaling Benefits**: With Managed Scaling, the system intelligently adds and removes nodes based on YARN memory or HDFS utilization metrics. AWS improvements to Managed Scaling can [reduce costs by up to 19%](https://aws.amazon.com/blogs/big-data/reduce-amazon-emr-cluster-costs-by-up-to-19-with-new-enhancements-in-amazon-emr-managed-scaling/) by more effectively managing cluster sizes and minimizing over-provisioning. - **Fine-Tuning Scaling Policies**: Set up scaling policies to fine-tune when to scale up or down. This ensures that the scaling process aligns with your specific workload patterns, preventing unnecessary costs from misconfigured scaling strategies. ## **Enhancing Storage Efficiency** Source: [HBase on Amazon S3 (Amazon S3 storage mode)](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hbase-s3.html)  Storage is another critical aspect of AWS EMR costs, as large datasets and frequent I/O operations can rack up significant charges. Implementing best practices for data storage optimization can help reduce these costs while improving job performance. ### **Using Data Compression Formats Like Parquet, ORC, Avro** Data compression is an essential strategy for reducing AWS EMR storage costs, especially when dealing with massive amounts of data. Formats like Parquet, ORC, and Avro offer significant compression and speed benefits, which ultimately help lower your storage and transfer costs. - **Parquet and ORC**: Both Parquet and ORC are columnar formats optimized for analytical workloads. They reduce storage costs by compressing data efficiently, which can save you up to 50% on storage costs while also improving query performance by reducing the amount of data that needs to be scanned. - **Avro**: For row-based data, Avro is a flexible and efficient option that offers good compression rates and compatibility with Hadoop-based applications. The use of compression codecs like Snappy or Gzip further enhances efficiency, reducing the overall data footprint. ### **Benefits of Data Partitioning and Efficient File Formats** Partitioning data is another effective method for controlling storage costs. Partitioning allows you to store data in smaller, more manageable segments, reducing the time it takes to retrieve relevant data. - **Partitioning Strategy**: By partitioning your data by time (e.g., daily or monthly) or by other logical keys, you can significantly reduce the amount of data that needs to be scanned. This results in lower EC2 compute costs because the cluster only processes the relevant subset of data. - **Efficient File Formats**: Utilizing formats like Parquet and ORC in combination with partitioning can provide a 5x improvement in query performance, as seen in the social media analytics provider example. These formats optimize how data is queried and processed, making jobs more efficient and cost-effective. ### **Choosing Appropriate S3 Storage Classes Such as Intelligent-Tiering and Glacier** When it comes to storing large datasets, choosing the right S3 storage class can dramatically impact costs. S3 Intelligent-Tiering and S3 Glacier offer cost-effective solutions for storing data with varying access patterns. - **S3 Intelligent-Tiering**: This storage class automatically moves data between two access tiers (frequent and infrequent access) to optimize costs based on usage patterns. If your data is accessed occasionally but requires quick retrieval, Intelligent-Tiering ensures you don’t overpay for high-performance storage. - **S3 Glacier**: For data that is rarely accessed but needs to be retained for long-term storage, S3 Glacier offers one of the[ most cost-effective storage solutions](https://aws.amazon.com/s3/storage-classes/glacier/). This option is ideal for archiving, reducing more storage costs compared to standard S3 classes. By leveraging these storage classes, you can lower your overall S3 costs, especially for data that does not need to be frequently accessed. ### Reduce AWS EMR Costs with Smarter Cluster Optimization See how engineers reduce EMR costs using right-sizing, Spot instances, auto-termination, and workload-aware optimization to eliminate idle clusters and improve resource efficiency without impacting performance. ## **Maximizing Cluster and Resource Efficiency** Optimizing for cost in EMR can be achieved by improving resource management strategies, making sure that your clusters are efficiently utilized, and that unnecessary expenses are minimized. Here are some practical steps to maximize cluster and resource efficiency, ensuring you’re only paying for what you actually need. ### **Developing Cost-Effectively Using Smaller Instance Types for EMR Notebooks** When developing with EMR Notebooks, consider using smaller instance types. EMR Notebooks are ideal for interactive data science and analytics, but they often don’t require large instances, especially when performing initial tests or smaller-scale processing. By selecting cost-efficient, [smaller instance types](https://aws.amazon.com/blogs/aws/new-t3-instances-burstable-cost-effective-performance/) like t3.micro or t3.small, you can significantly reduce your EMR costs without sacrificing performance. Additionally, by selecting the right EC2 instance type based on your workload’s needs, you can ensure that your resources are allocated more efficiently, preventing overspending on larger-than-necessary instances. This approach not only helps optimize for cost in EMR by reducing the overall instance size but also allows you to scale up when needed without paying for unnecessary resources. ### **Implementing Cluster Auto-Termination to Prevent Idle Charges** One of the most effective ways to optimize for cost in EMR is by enabling auto-termination for your clusters. AWS EMR provides the ability to automatically terminate clusters once a job is completed, preventing unnecessary costs during idle times. When clusters continue to run without performing any tasks, you’re still paying for the EC2 instances and EBS volumes. Enabling auto-termination ensures that your clusters will stop once they’re no longer needed, thus eliminating idle costs. Additionally, it’s essential to monitor cluster usage to identify periods of inactivity. By terminating clusters at the right time, you can save up to 20-30% of your monthly costs, especially for workloads that don’t require continuous operation. ### **Configuring Job Auto-Stop Policies and Notebook Sharing to Optimize Resources** To further optimize for cost in EMR, consider setting up job auto-stop policies for your workflows. Auto-stop policies ensure that once a job completes, the cluster will automatically stop, preventing any additional costs. Coupled with notebook sharing, which reduces the need for multiple users to run separate clusters, this setup can significantly reduce resource wastage. By encouraging teams to share notebooks and reduce the number of running clusters, you create an efficient system where resources are only utilized when necessary. This not only prevents resource underutilization but also minimizes idle time and costs associated with unnecessary compute and storage. Also Read: [Sedai Demo: AWS ECS Cost & Performance Optimization](https://www.sedai.io/video/sedai-for-aws-ecs-overview)  ## **Leveraging Spot Instances** Source: [How to leverage Spot Instances in Data Pipelines on AWS](https://medium.com/ias-tech-blog/emr-series-how-to-leverage-spot-instances-in-data-pipelines-on-aws-7c06cf0959d6)  Spot Instances are one of the most effective ways to optimize for cost in EMR. These instances allow you to take advantage of unused EC2 capacity at a fraction of the cost, but managing them effectively requires careful consideration. Here’s how to leverage Spot Instances to their full potential: ### **Configuring EMR Cluster to Utilize Spot Instances** When looking to reduce costs, Spot Instances can play a significant role in how to optimize for cost in EMR. Spot Instances can be used for non-critical tasks, such as task nodes in your EMR cluster. By selecting Spot Instances for these tasks, you can achieve [savings of up to 40-90%](https://aws.amazon.com/aws-cost-management/aws-cost-optimization/spot-instances/) compared to On-Demand instances. However, you need to configure your EMR cluster appropriately. Use Instance Fleets to mix On-Demand and Spot Instances. This allows your cluster to automatically scale using the least expensive Spot Instances, while still maintaining the capacity to handle jobs with On-Demand instances when necessary. Spot Instances are best suited for workloads that are fault-tolerant and can handle interruptions. ### **Cost Savings Strategies Through Reliable Spot Instance Optimization** To maximize savings, ensure that you’re optimizing Spot Instances through proper configuration. This involves adjusting the maximum Spot price to ensure you're not bidding more than you’re willing to pay, and setting up auto-replacement policies to automatically switch from Spot to On-Demand instances if your Spot Instances are interrupted. One of the main challenges of Spot Instances is the risk of interruption. However, by using YARN node labels and implementing checkpointing in your Spark jobs, you can recover from interruptions without losing significant progress, ensuring that Spot Instance interruptions don’t derail your workload and cost management. ### **Handling Spot Instance Interruptions Effectively** Spot Instances are subject to termination if AWS needs the capacity back, which can be problematic if your tasks are not designed to handle such interruptions. To reduce the impact of these interruptions and maintain cost efficiency, it’s essential to design your jobs with resilience in mind. Implement checkpointing and stateful job management, so when an interruption occurs, your job can resume seamlessly from where it left off. Additionally, auto-scaling policies combined with Spot Instance interruption handling can further ensure that jobs continue running even if Spot Instances are reclaimed. You can use a combination of AWS Lambda functions and CloudWatch to monitor your Spot Instance usage and be prepared for any interruptions. By using Spot Instances alongside these strategies, you can realize substantial cost savings while maintaining the reliability and performance of your AWS EMR workloads. ## **Performance Tuning: Optimizing Your AWS EMR Cluster for Efficiency** To effectively optimize for cost in EMR, it’s essential to focus on performance tuning. Optimizing job configurations, monitoring key metrics, and reducing processing times can significantly improve the cost efficiency of your cluster. Here’s how you can tune your performance to lower costs without sacrificing job performance. ### **Fine-Tuning Job Configurations: Focusing on Memory and Shuffle Operations** One of the most impactful ways to optimize for cost in EMR is by fine-tuning job configurations, specifically memory settings and shuffle operations. Memory usage in Spark applications is crucial—inefficient memory allocation can lead to excessive resource usage, which directly impacts costs. Ensure that memory settings are appropriately configured to match your workload’s needs. - **Optimize shuffle operations**: Shuffle operations are resource-intensive, especially during tasks like aggregations or joins. Adjusting the spark.shuffle.compress and spark.shuffle.file.buffer configurations can reduce the memory footprint, ultimately leading to lower resource consumption. - **Memory configurations**: Fine-tune the executor memory and executor cores to align with your workload’s requirements. For instance, larger memory allocations for memory-intensive jobs will prevent unnecessary spilling to disk, thus reducing I/O operations and improving overall performance. By optimizing memory and shuffle operations, you can reduce both processing times and resource consumption, ultimately helping you control how to optimize for cost in EMR. ### **Using CloudWatch to Monitor Key EMR Metrics for Cost Anomalies** Another powerful tool in optimizing for cost in EMR is AWS CloudWatch. By setting up custom CloudWatch alarms for key performance metrics, you can actively monitor the health of your EMR cluster and detect potential cost anomalies in real-time. Key metrics to monitor include: - **YARNMemoryAvailablePercentage**: This helps you monitor memory usage and prevent over-provisioning. - **HDFSUtilization**: Overuse of HDFS can indicate inefficiency, leading to unnecessary costs. - **ContainerPendingRatio**: High pending ratios may point to resource bottlenecks that could increase processing times and costs. CloudWatch gives you real-time visibility into your cluster’s performance, helping you catch inefficiencies before they turn into significant cost overruns. ### **Reducing Processing Times Through Optimized Spark Configurations** Optimized Spark configurations are essential for optimizing for cost in EMR. By adjusting Spark settings such as dynamic allocation and task parallelism, you can ensure that your jobs run more efficiently, cutting down on unnecessary resource consumption. - **Dynamic allocation**: Enable Spark's dynamic resource allocation to automatically adjust the number of executors based on workload demands, thus preventing over-provisioning. - **Task parallelism**: Adjust the number of tasks per executor to ensure better parallelization, reducing the overall execution time and, consequently, the cost. Fine-tuning these configurations can reduce processing times significantly, helping you run more cost-effective jobs on your AWS EMR cluster. EMR clusters often waste spend when scaling and idle nodes go unnoticed. [Book a demo ](https://sedai.io/demo)to see how Sedai optimizes EMR costs by right-sizing clusters and reducing idle capacity across ## **Improving Cost Visibility and Control: Keeping AWS EMR Costs in Check** Source: [Attribute Amazon EMR on EC2 costs to your end-users](https://aws.amazon.com/blogs/big-data/attribute-amazon-emr-on-ec2-costs-to-your-end-users/)  To optimize for cost in EMR, developing strategies to gain better visibility and control over your costs is essential. By setting up clear cost tracking and alerts, you can proactively manage and reduce unnecessary expenses. ### **Developing and Implementing a Comprehensive Tagging Strategy** A comprehensive tagging strategy is crucial for understanding how to optimize for cost in EMR. Tags help categorize and track costs associated with different projects, teams, or workloads. With accurate tags in place, you can easily attribute costs to the appropriate departments or activities, enabling better cost allocation and accountability. - **Develop a tagging schema**: For instance, use tags like "Environment," "Team," and "Project" to track usage and allocate costs more effectively. - **Enforce tagging policies**: Ensure that all resources, including EC2 instances, S3 buckets, and EBS volumes, are properly tagged before deployment. With an effective tagging strategy, you’ll be able to gain clear insights into your EMR usage, making it easier to identify cost-saving opportunities. ### **Utilizing AWS Budgets to Establish Cost Thresholds and Alerts** AWS Budgets allows you to set specific cost thresholds and configure alerts to notify you when your usage exceeds the budget. Setting up AWS Budgets for your EMR clusters ensures that you stay within your cost limits and don’t face unexpected spikes in your bill. - **Set budget thresholds**: Define budgets for specific resources like EC2 instances, S3 storage, and EMR services to keep costs under control. - **Configure alerts**: Set up alerts to notify you when your spending exceeds the budget at different thresholds (e.g., 50%, 80%, 100%). This proactive approach enables you to take immediate action before costs get out of hand. AWS Budgets helps you stay on top of your EMR expenses and ensures you can quickly adjust your resources to prevent cost overruns. ### **Tracking Costs and Performance with AWS Cost Explorer** AWS Cost Explorer provides a powerful tool for visualizing your AWS spend and performance metrics. By using Cost Explorer, you can track your EMR costs over time, identify patterns, and make data-driven decisions on how to optimize for cost in EMR. - **Monitor cost trends**: Use Cost Explorer to identify spikes in your EMR costs and compare historical data to find areas where savings can be made. - **Track performance**: Correlate cost data with performance metrics to ensure that cost optimizations are not compromising the performance of your cluster. It also helps you to monitor and comprehend your costs at a high level, which allows you to incrementally optimize for cost in EMR over time. ### **Conclusion** AWS EMR cluster cost management can be a challenge, but you can save huge, relatively more on costs by understanding the main components of the cost and using resources optimally. From EC2 instances to storage and scaling, small changes can make huge cost savings while not compromising performance.  If you’re looking for more in-depth strategies to optimize your cloud infrastructure and control costs, be sure to explore [Sedai's AI-driven cloud optimization solutions](https://www.sedai.io/) designed to autonomize cost management and enhance efficiency in your cloud operations. ### Frequently Asked Questions Use Spot Instances for task nodes, right-size instance groups, enable EMR managed scaling, and shut down idle clusters. Transient clusters that run only for a job avoid paying for idle capacity. #### How do you optimize cost in Amazon EMR? Combining Spot Instances for flexible task nodes with On-Demand or Reserved core nodes, plus auto-termination after jobs finish, typically yields the lowest cost. Storing data in S3 instead of HDFS also reduces persistent cost. #### What is the cheapest way to run Amazon EMR? Yes. Managed scaling automatically adds and removes nodes based on workload, so you pay for capacity only when jobs need it instead of running fixed-size clusters. #### Does EMR managed scaling reduce costs? Sedai applies autonomous, demand-aware optimization to align cluster resources with real job requirements, reducing spend without slowing processing. #### How does Sedai help optimize Amazon EMR costs? --- # Best Practices for Tagging AWS Resources Source: https://sedai.io/blog/aws-tagging-best-practices Learn AWS tagging best practices to improve cost allocation, governance, & automation. Build a tagging strategy that supports accurate optimization & scaling decisions. You have rightsizing recommendations queued, a cost explorer dashboard, & an optimization tool connected to your AWS environment. None of it is working as expected because three teams are tagging the same environment differently. This isn't a tooling problem. It's a metadata problem. Your AWS tagging strategy is the data contract that every downstream system depends on. Cost allocation, rightsizing recommendations, & autonomous optimization all read resource metadata before acting. When that metadata is wrong, tools don't fail loudly. They fail silently, producing confident outputs from bad inputs. Before you invest more in optimization tools, fix the contract they depend on. In this article, we will cover: - [When Tags Are Wrong, Optimization Targets the Wrong Things](https://sedai.io/blog/aws-tagging-best-practices#:~:text=Data%20Contract%20First-,When%20Tags%20Are%20Wrong%2C%20Optimization%20Targets%20the%20Wrong%20Things,-Most%20teams%20treat) - [The Tag Taxonomy That Actually Sticks](https://sedai.io/blog/aws-tagging-best-practices#:~:text=on%20incomplete%20metadata-,The%20Tag%20Taxonomy%20That%20Actually%20Sticks,-You%20don%27t%20need) - [Enforcing taxonomy at the Infrastructure Level](https://sedai.io/blog/aws-tagging-best-practices#:~:text=Book%20a%20Demo-,Enforcing%20taxonomy%20at%20the%20Infrastructure%20Level,-Defining%20a%20taxonomy) - [Tagging as the Metadata Contract for Autonomous Optimization](https://sedai.io/blog/aws-tagging-best-practices#:~:text=is%20ungoverned%20infrastructure.-,Tagging%20as%20the%20Metadata%20Contract%20for%20Autonomous%20Optimization,-If%20you%20want) - [Fix the Data Contract First](https://sedai.io/blog/aws-tagging-best-practices#:~:text=made%20it%20possible.-,Fix%20the%20Data%20Contract%20First,-Execution%20engines%20don%27t) ## **When Tags Are Wrong, Optimization Targets the Wrong Things** Most teams treat tagging as a one-time setup task. Later rarely arrives. [Flexera's State of the Cloud 2026](https://info.flexera.com/CM-REPORT-State-of-the-Cloud#:~:text=Get%20started%20today-,Top%20cloud%20challenges,-All%3A%20N%3D753) shows that 85% of organizations cite cost management, governance & lack of expertise as top cloud challenges. While the report doesn't isolate tagging directly, inconsistent metadata is a common underlying cause of governance & cost attribution failures. The[State of FinOps 2026 Report](https://data.finops.org/#:~:text=Full%20allocation%20of%20cloud%20spending) puts a harder number on the downstream impact: only 14% of organizations achieve full allocation of cloud costs at the unit level, meaning most can't attribute spend to a specific service, team, or environment. Bad tags do not break dashboards. They break decisions. A production service tagged env:dev corrupts your reporting & redirects which resources your tools recommend for action. A rightsizing tool reads resource identity from what it can see. If it reads a production API as a dev workload, it applies dev-tier sizing targets. That is a production risk: a scale-down recommendation hitting a customer-facing service. Most optimization tools don't catch this. They read utilization numbers, compare against thresholds, & produce recommendations. That's the entire loop. There is no step where the tool asks what the resource actually does or whether it should be touched at all.[McKinsey research](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/everything-is-better-as-code-using-finops-to-manage-cloud-costs) consistently points to poor governance foundations as a leading driver of cloud waste, with spend leaking through misattributed & untracked resources. [The Hard Truth: FinOps Inform Doesn't Pay the Bills](https://sedai.io/blog/the-hard-truth-finops-inform-doesnt-pay-the-bills) makes the point plainly - visibility without action is just another dashboard. Visibility built on bad metadata is worse. When tagging fails, three things break: - **Cost attribution** becomes unreliable - **Execution targets** the wrong resources - **Autonomous systems** act on incomplete metadata ## **The Tag Taxonomy That Actually Sticks** You don't need a 40-tag schema. You need five mandatory tags, consistently applied, with enforced value standards. Any tag that depends on human discipline will fail at scale. Start here: - **environment**: prod, staging, dev (no variations, no synonyms) - **owner**: cost accountability, who pays when this resource is over-provisioned - **service**: the application or service this resource belongs to - **cost-center**: maps to your internal billing structure - **team**: execution responsibility, who responds when it breaks The values matter as much as the keys. prod, production, & Production are three different strings in any cost aggregation query. One inconsistency across a large fleet means your cost-center reports are missing spend. Define the allowed values for each tag upfront. Put them in your IaC module defaults, not in a wiki page three levels deep. Before evaluating any optimization tool's recommendations, verify your tagging baseline first.[22 Best AWS Cost Optimization Tools & 12+ Strategies](https://sedai.io/blog/how-to-optimize-for-cost-in-ec2-aws) covers the tooling layer in depth, but every tool on that list depends on accurate metadata to produce anything meaningful.[Gartner's cloud cost management guidance](https://www.gartner.com/en/infrastructure-and-it-operations-leaders/insights/manage-and-optimize-a-cloud-environment) consistently identifies tagging maturity as a prerequisite for optimization success. See how Sedai helps standardize AWS tagging for accurate cost allocation, better governance & reliable optimization decisions. ## **Enforcing taxonomy at the Infrastructure Level** Defining a taxonomy is not enforcement. Tags that rely on human memory don't survive at scale. [AWS Tag Policies](https://docs.aws.amazon.com/organizations/latest/userguide/orgs_manage_policies_tag-policies.html), applied through AWS Organizations, define allowed values for tag keys & flag non-compliant resources automatically. Pair this with Service Control Policies (SCPs) to block resource creation without required tags entirely. Your IaC modules should embed the mandatory tag set as a required input block, not as documentation. If environment & owner aren't passed at provisioning time, the module fails. That's the point. For resources that escape the pipeline, the[AWS Resource Groups Tagging API](https://docs.aws.amazon.com/resourcegroupstagging/latest/APIReference/overview.html) provides a programmatic audit surface. Run it on a schedule. Treat untagged resources as contract violations, not loose ends. Teams with mature tagging governance see higher returns on their optimization spend, per[IDC research](https://blogs.idc.com/2023/02/22/the-era-of-finops-focus-is-shifting-from-cloud-features-to-cloud-value/). Untagged infrastructure is ungoverned infrastructure. ## **Tagging as the Metadata Contract for Autonomous Optimization** If you want to really optimize, you stop treating tagging as a housekeeping task. Sedai depends on accurate resource metadata to make safe, targeted changes. It operates on performance metrics & workload classification, not logs or PII. It reads golden signals: latency, errors, traffic, & saturation. But it also reads resource identity: which environment, which service, which team owns this resource. When tags are wrong, that classification is incomplete. Optimization engines do not understand intent. They trust metadata. The metrics may be accurate, but acting without knowing what a resource actually is means acting on an incomplete picture. That's the signal gap. Not whether the tool can detect an oversized instance, but whether it can tell a production API from a dev batch job before it acts. As[ Autonomy Is a Context Problem, Not a Model Problem](https://sedai.io/blog/autonomy-context-problem-not-model-problem) makes clear, without complete metadata, even a capable execution engine operates on assumptions. Precise decisions depend on the contract holding. Break it, & the execution engine is flying blind.[ KnowBe4 saved over $1.2M on AWS](https://sedai.io/blog/knowbe4) with Sedai's autonomous optimization, & clean tagging was part of the foundation that made it possible. ## **Fix the Data Contract First** Execution engines don't fail because they lack capability. They fail because they act on incomplete information. Bad metadata hits twice. First, the signal gap: the system misreads which workloads to target. Then the execution gap: it acts on those bad signals at scale, across every resource it touches. Fix the data contract before you trust the execution engine. Five enforced tags with standardized values, embedded in IaC defaults, audited on a schedule. That's a manageable lift. Without it, every action your stack takes is a guess. A[self-driving cloud](https://sedai.io/) needs clean metadata to decide & act safely. Clean tagging hygiene isn't just good housekeeping, it's the prerequisite for any optimization to work reliably. Fix the contract first.