# Sedai: Blog (part 20 of 27)

Full content of 10 pages from https://sedai.io/blog, published 2026-03-05 to 2026-03-23. 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.

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# What Is Cloud Cost Analysis in 2026
Source: https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026

What is cloud cost analysis? Learn how to move beyond visibility to actionable optimization with frameworks, KPIs, and automation strategies.

Every cloud cost review follows the same arc. Someone pulls a dashboard. The team flags anomalies, tags the biggest offenders, & builds a list of recommendations. Then the meeting ends, the list enters a backlog, & next month's review surfaces the same problems — plus a few new ones.

That cycle isn't a failure of analysis. Most teams have solid visibility into where their cloud budget goes. The failure is execution: every optimization carries production risk, & validating changes across dozens of services takes more engineering time than the savings justify on a case-by-case basis. So the waste compounds quietly while the backlog grows.

This guide covers what effective cloud cost analysis looks like in 2026, where most programs get stuck, & what it takes to turn insight into sustained savings.

Most cost visibility tools show you the problem. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking autonomous action based on your actual workload behavior.

**In this article:**

- [What Is Cloud Cost Analysis?](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=FAQs-,What%20Is%20Cloud%20Cost%20Analysis%3F,-Cloud%20cost%20analysis)
- [Why Cloud Cost Analysis Matters](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=delivering%20proportional%20value.-,Why%20Cloud%20Cost%20Analysis%20Matters,-Cloud%20spending%20is)
- [Key Components of Cloud Cost Analysis](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=that%20matters%20most.-,Key%20Components%20of%20Cloud%20Cost%20Analysis,-Compute%20Costs)
- [How to Conduct a Cloud Cost Analysis](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=How%20to%20Conduct%20Cloud%20Cost%20Analysis)
- [Cloud Cost Analysis vs. Cloud Cost Optimization](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=a%20periodic%20audit.-,Cloud%20Cost%20Analysis%20vs.%20Cloud%20Cost%20Optimization,-These%20terms%20are)
- [Common Cloud Cost Analysis Mistakes](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=waste%20actually%20lives.-,Common%20Cloud%20Cost%20Analysis%20Mistakes,-Analyzing%20spend%20without)
- [How Can Sedai Help You With Cloud Cost Analysis?](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=see%20our%20comparison.-,How%20Can%20Sedai%20Help%20You%20With%20Cloud%20Cost%20Analysis%3F,-The%20hardest%20part)
- [FAQs](https://sedai.io/blog/what-is-cloud-cost-analysis-in-2026#:~:text=execution%20gap.-,FAQs,-How%20often%20should)

## **What Is Cloud Cost Analysis?**

Cloud cost analysis is the process of tracking, attributing, & evaluating cloud spend across services, teams, & environments to improve decision-making. It breaks your cloud bill into understandable segments so you can identify what's driving costs, where waste is accumulating, & which workloads are consuming more budget than they should.

A thorough cloud cost analysis typically covers:

- **Cost attribution**: mapping every dollar to the team, service, or environment that generated it
- **Trend identification**: spotting spending patterns, anomalies, & month-over-month changes
- **Utilization assessment**: measuring how efficiently provisioned resources are actually being used
- **Forecasting**: projecting future spend based on historical patterns & planned changes

The distinction between cloud cost analysis & simply reading your cloud bill is granularity. Your monthly invoice tells you how much you spent. Analysis tells you why — and whether that spend is delivering proportional value.

## **Why Cloud Cost Analysis Matters**

Cloud spending is growing faster than most organizations planned for. The[ Flexera 2025 State of the Cloud Report](https://info.flexera.com/CM-REPORT-State-of-the-Cloud) found that organizations waste approximately 27% of their cloud spend — and most respondents estimated their waste was lower than what Flexera's data showed. That disconnect between perceived & actual waste is exactly what cloud cost analysis is designed to close.

Without structured analysis, three problems compound over time:

- **Budget drift**: cloud spend increases quarter over quarter without a clear connection to business growth. Teams add resources faster than they retire them, & nobody tracks the cumulative impact until the bill arrives.
- **Accountability gaps**: when costs aren't attributed to specific teams or services, nobody owns the spending. Engineering makes provisioning decisions, finance gets the invoice, & the two sides never reconcile.
- **Optimization paralysis**: teams can see the waste but can't prioritize which actions to take first — or can't act at all because they lack confidence that changes won't break production.

Cloud cost analysis addresses the first two directly. The third — optimization paralysis — is where most analysis programs stall, & it's the problem that matters most.

## **Key Components of Cloud Cost Analysis**

### **Compute Costs**

Compute typically represents the largest share of cloud spend. This includes virtual machines, containers, serverless functions, & managed compute services. The primary cost drivers are instance type selection, provisioning levels, & how effectively workloads use the resources allocated to them.

The most common source of compute waste is overprovisioning — running larger instances or more replicas than the workload requires. This happens because teams size for peak traffic at provisioning time & never revisit those decisions as usage patterns change.

### **Storage & Data Transfer Costs**

Storage costs accumulate from block storage, object storage, snapshots, & backups. Data transfer costs — particularly egress charges for data leaving the cloud or moving between regions — are frequently underestimated. Both categories tend to grow steadily over time as data volumes increase, making them easy to overlook in monthly reviews focused on compute.

### **Licensing & Managed Services**

Database licenses, marketplace subscriptions, logging services, & managed platform fees add up faster than most teams expect. These costs are often spread across multiple billing line items, making them harder to identify without deliberate analysis.

### **Commitment-based Discounts**

Reserved Instances, Savings Plans, & committed use discounts reduce unit costs but introduce financial risk if utilization drops below committed levels. Analyzing commitment coverage — how much of your spend is covered by discounts & how well those commitments match actual usage — is a critical part of cloud cost analysis that many teams skip.

### Understand Cloud Cost Analysis

See how Sedai explains cloud cost analysis in 2026 for scale, control & operational efficiency.

## **How to Conduct Cloud Cost Analysis**

### **1. Collect & Normalize Billing Data**

Start by exporting detailed usage & billing data from your cloud provider. For AWS, this means the Cost & Usage Report (CUR). Azure & GCP have equivalent exports. Don't rely on high-level dashboards alone — they summarize, but you need resource-level granularity to find actionable waste.

If you run a multi-cloud environment, normalize the data into a common format so you can compare costs across providers without getting lost in different pricing models and SKU structures.

### **2. Tag & Attribute Every Resource**

Consistent tagging is the foundation of useful cost analysis. Every resource should be tagged by team, service, environment (dev/staging/prod), & cost center. Without tagging, you have total spend numbers but no way to assign accountability.

In practice, tagging compliance below 90% makes your cost allocation data unreliable. If your tagging is inconsistent today, fix that first — analysis built on incomplete attribution will produce misleading conclusions.

### **3. Segment & Analyze by Business Context**

Break your spend into segments that match how your organization actually operates: by team, by product, by environment, by service. Look at how each segment trends over time. Compare forecasted costs to actuals. Flag services that are growing faster than the business metrics they support.

The goal here is to answer the question: is this spending proportional to the value it delivers? A service that costs $50,000/month & handles 40% of your revenue is very different from one that costs $50,000/month & supports an internal reporting dashboard.

### **4. Identify Waste & Prioritize by Impact**

Once you can see where money is going, identify the categories of waste. Common ones include idle resources, oversized instances, unattached storage volumes, low-utilization reserved capacity, & unused snapshots or backups.

Prioritize by dollar impact, not by count. Ten oversized EC2 instances costing $500/month each matter more than 200 unattached EBS volumes at $2/month each — even though the volume count looks worse on a dashboard. For a structured approach to turning these findings into savings, see our[ cloud cost optimization framework](https://sedai.io/blog/cloud-cost-optimization-strategies-practices).

### **5. Build Feedback Loops**

A one-time analysis tells you where you stand today. Ongoing value comes from making analysis a recurring process — weekly anomaly detection, monthly deep dives, & quarterly strategic reviews. Set up alerts for spending anomalies so you catch problems early rather than discovering them at month-end.

The organizations that sustain cost efficiency are the ones that treat cloud cost analysis as a continuous practice, not a periodic audit.

## **Cloud Cost Analysis vs. Cloud Cost Optimization**

These terms are often used interchangeably, but they serve different functions:

**Cloud cost analysis** answers the question: where is the money going, & is it well spent? It's diagnostic. You're identifying waste, tracking trends, & assigning accountability.

**Cloud cost optimization** answers: how do we reduce spend without hurting performance? It's the execution layer — rightsizing instances, adjusting autoscaling thresholds, purchasing reserved capacity, & eliminating idle resources.

When analysis and optimization work together, cost decisions stick — because they're grounded in what's actually happening across the system. You need both, and the most common failure mode is strong analysis paired with weak execution. Teams generate hundreds of recommendations per month but implement fewer than 10% because every change carries production risk. That backlog of unactioned insights is where most cloud waste actually lives.

## **Common Cloud Cost Analysis Mistakes**

**Analyzing spend without business context.** Raw cost data is meaningless without knowing what each dollar supports. A $200,000/month compute bill might be perfectly efficient for a high-traffic SaaS platform & wildly wasteful for an internal tool. Always tie cost analysis to business outcomes — revenue, transactions, active users — not just resource utilization.

**Treating analysis as a quarterly event.** Cloud environments change daily. A cost analysis done in January tells you almost nothing useful in March. The teams getting the most value review spend weekly & set up automated anomaly detection to catch unexpected increases within days, not months.

**Focusing on unit cost instead of total cost of ownership.** Switching to a cheaper instance type saves money on paper. If that change requires three engineers to spend a week testing, validating, & deploying it across 50 services, the total cost of making that optimization may exceed the savings. Factor in the engineering time required to act on every recommendation — it's the hidden tax on manual optimization.

**Ignoring commitment coverage.** Many teams focus exclusively on on-demand waste while ignoring that their Reserved Instances or Savings Plans are underutilized. Commitment analysis should be part of every cost review. For a breakdown of[ cloud cost management platforms](https://sedai.io/blog/best-cloud-cost-management-platforms) that help track coverage, see our comparison.

## **How Can Sedai Help You With Cloud Cost Analysis?**

The hardest part of cloud cost analysis isn’t insight, it’s execution. Most teams already see the waste, but acting on it is slow and risky. Validating changes across services takes more time than most teams have.

Sedai closes that gap by continuously learning how workloads behave, and autonomously optimizes resources based on real application data. Rightsizing, autoscaling adjustments, & resource reclamation happen continuously, not as a quarterly project that competes for engineering bandwidth.

[KnowBe4](https://sedai.io/customers/knowbe4), achieved 27% cost reduction with over 1,100 autonomous actions per quarter in under five months, not because their analysis improved, but because the actions that analysis recommended were finally being executed safely & continuously.

If your team has strong visibility into cloud costs but struggles to turn those insights into sustained savings,[ see how Sedai's platform closes the execution gap](https://sedai.io/platform).

AI-detected patterns — anomalous spend spikes, seasonal usage shifts, service-level waste — surface insights that manual cost analysis misses entirely. [Book a demo](https://sedai.io/demo) to see how Sedai acts on those patterns continuously in your environment.

## **FAQs**

### **How often should you perform cloud cost analysis?**

At minimum, conduct a detailed review monthly & check for spending anomalies weekly. Cloud environments change daily, so analysis done quarterly will miss cost spikes, configuration drift, & idle resources that accumulate between reviews. Automated anomaly detection reduces the manual effort & catches unexpected increases within days.

### **What is the difference between cloud cost analysis & FinOps?**

Cloud cost analysis is one practice within the broader FinOps discipline. FinOps encompasses the organizational framework — cross-functional collaboration between engineering, finance, & business teams — while cloud cost analysis focuses specifically on understanding where money is being spent & whether that spending is efficient.

### **What are the most common causes of unexpected cloud cost increases?**

The three most frequent causes are auto-scaling events that overprovision & don't scale back down, orphaned resources left running after projects end or teams restructure, & data transfer costs that grow as architecture becomes more distributed. Consistent tagging & weekly spend reviews catch most of these before they compound.

### **Can cloud cost analysis reduce costs without optimization tools?**

Analysis alone surfaces waste but doesn't fix it. Teams can act on findings manually — shutting down idle resources, resizing instances, adjusting reservations — but manual optimization is slow, risky, & difficult to sustain. The organizations that achieve lasting reductions pair analysis with[ continuous optimization practices](https://sedai.io/blog/cloud-cost-management-and-optimization-best-practices) that turn insights into safe, ongoing actions.

---

# On-Premise vs. SaaS in 2026
Source: https://sedai.io/blog/on-premise-vs-saas

Understand on-premises vs. SaaS, key differences, and how to choose the right deployment model for your business. Learn more.

If you're running infrastructure in 2026, you're probably managing on-premise and SaaS whether you planned to or not. The migration that was supposed to move everything to SaaS left behind the workloads that couldn't move — regulated data, latency-sensitive systems, legacy dependencies. And now your platform team owns the operational complexity of both.

The organizations handling this well aren't picking a side. They're making workload-level decisions based on cost, compliance, control, & long-term operational burden.

On-prem gives you infrastructure you own & manage. SaaS gives you the same functionality hosted & maintained by the vendor. Simple enough as categories. But every workload carries a different answer depending on who's using it, what data it touches, & what happens when something breaks at 2 AM.

This guide breaks down the real differences between on-premise & SaaS in 2026, the factors that should drive your decision, and what both models demand from an operational standpoint.

**In this article:**

[What Is On-Premise Software?](https://sedai.io/blog/on-premise-vs-saas#:~:text=FAQs-,What%20Is%20On%2DPremise%20Software%3F,-On%2Dpremise%20software)

[What Is SaaS?](https://sedai.io/blog/on-premise-vs-saas#:~:text=is%20on%20you.-,What%20Is%20SaaS%3F,-SaaS%20(Software%20as)

[Key Differences of On-Premise vs. SaaS](https://sedai.io/blog/on-premise-vs-saas#:~:text=of%20vendor%20relationships.-,Key%20Differences%20of%20On%2DPremise%20vs.%20SaaS,-On%2DPremise)

[Factors to Consider When Opting for Either On-Premise or SaaS](https://sedai.io/blog/on-premise-vs-saas#:~:text=Book%20a%20Demo-,Factors%20to%20Consider%20When%20Opting%20for%20Either%20On%2DPremise%20or%20SaaS,-Budget)

[Why Are SaaS Solutions Replacing On-Premise Setups?](https://sedai.io/blog/on-premise-vs-saas#:~:text=else%20in%20SaaS.-,Why%20Are%20SaaS%20Solutions%20Replacing%20On%2DPremise%20Setups%3F,-Gartner%20projects%20worldwide)

[How Can Sedai Help You Choose Between On-Premise vs. SaaS?](https://sedai.io/blog/on-premise-vs-saas#:~:text=data%20sovereignty%20constraints.-,How%20Can%20Sedai%20Help%20You%20Choose%20Between%20On%2DPremise%20vs.%20SaaS%3F,-The%20on%2Dpremise)

[FAQs](https://sedai.io/blog/on-premise-vs-saas#:~:text=it%20continuously.-,FAQs,-What%20is%20the)

## **What Is On-Premise Software?**

On-premise software is installed & runs on servers, networking equipment, & storage that your organization owns or leases, typically in a private data center or colocation facility. Your IT team is responsible for everything: hardware procurement, installation, configuration, patching, security, backups, & capacity planning.

The model gives you full control over your infrastructure & data. You decide what hardware to use, how to configure it, where data lives, & who has access. That control is the primary reason organizations choose on-premise, especially in regulated industries like healthcare, financial services, & government where data residency & sovereignty requirements can be non-negotiable.

The tradeoff is cost & complexity. On-premise deployments require significant upfront capital expenditure for hardware, plus ongoing operational expense for the team that maintains it. Scaling means purchasing & provisioning new equipment, which can take weeks or months. And every upgrade, patch, & configuration change is on you.

Most cost visibility tools show you the problem. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking autonomous action based on your actual workload behavior.

## **What Is SaaS?**

SaaS (Software as a Service) delivers applications over the internet on a subscription basis. The vendor hosts the software, manages the underlying infrastructure, handles updates, & provides security. You access the application through a browser or API without managing any of the backend systems.

The model shifts the operational burden from your team to the vendor. You don't need to provision servers, manage patches, or plan capacity. The vendor handles availability, performance, & disaster recovery as part of the service. This is why SaaS adoption has accelerated across virtually every industry: it lets organizations focus on using software rather than running it.

In 2026, SaaS is the default deployment model for most business applications. CRM, collaboration tools, HR systems, marketing platforms, & increasingly even developer tools & data infrastructure are delivered as SaaS. The[ Flexera 2025 State of the Cloud Report](https://www.flexera.com/blog/finops/the-latest-cloud-computing-trends-flexera-2025-state-of-the-cloud-report/) found that SaaS spend continues to grow faster than any other cloud category, and most enterprises now manage many SaaS subscriptions.

The tradeoff is control. You're dependent on the vendor for uptime, security practices, data handling, & feature development. Customization is limited to what the platform exposes. And while SaaS eliminates infrastructure management, it introduces a different kind of complexity: subscription sprawl, integration challenges, & the operational overhead of managing dozens or hundreds of vendor relationships.

## **Key Differences of On-Premise vs. SaaS**

|  | On-Premise | SaaS |
| --- | --- | --- |
| Cost structure | High upfront CapEx, lower ongoing OpEx | Low upfront cost, recurring subscription OpEx |
| Implementation speed | Weeks to months (hardware procurement, installation, configuration) | Hours to days (sign up, configure, deploy) |
| Customization & control | Full control over infrastructure, deep customization | Limited to vendor-exposed configuration options |
| Maintenance & upgrades | Your team handles all patching, upgrades, & troubleshooting | Vendor manages updates, patches, & infrastructure maintenance |
| Scalability | Requires hardware procurement & provisioning lead time | Near-instant scaling through subscription changes or auto-provisioning |
| Data residency | Full control over where data is stored & processed | Dependent on vendor's data center locations & policies |
| Security responsibility | Entirely your organization's responsibility | Shared responsibility between your organization & the vendor |

### **Cost Structure**

On-premise requires significant capital expenditure upfront: servers, networking equipment, storage, data center space, power, & cooling. You're also carrying ongoing costs for the IT staff who maintain the environment. The total cost of ownership is often much higher than the initial hardware purchase once you factor in personnel, power, maintenance contracts, & eventual hardware refresh cycles.

SaaS shifts the cost model to operational expenditure. You pay a subscription fee, typically per user or per usage tier, with no upfront infrastructure investment. This makes SaaS more accessible for smaller organizations & faster to deploy.

[Gartner's analysis shows SaaS accounts ](https://www.gartner.com/en/newsroom/press-releases/2025-01-21-gartner-forecasts-worldwide-it-spending-to-grow-9-point-8-percent-in-2025)for roughly $300 billion of the $723 billion in public cloud spending expected for 2025, more than a third of total cloud spend. But subscription costs compound over time, and at scale, the cumulative spend on a SaaS platform can exceed what an on-premise deployment would have cost, especially for widely deployed applications.

### **Implementation Speed**

On-premise deployments are measured in weeks to months. Hardware has to be procured, racked, configured, & tested. Software needs to be installed, integrated with existing systems, & validated. The entire process requires coordination between procurement, IT, security, & the business teams that will use the software.

SaaS implementations are measured in hours to days for straightforward applications. Sign up, configure the workspace, integrate with identity management, & start using it. More complex SaaS deployments, like enterprise CRM or ERP systems, can still take months for full rollout, but the infrastructure component is eliminated entirely.

### **Customization & Control**

On-premise gives you access to everything: the operating system, the network configuration, the database layer, & the application itself. You can modify, extend, & integrate at any level. This matters when you have unique workflow requirements, need to integrate with legacy systems, or operate in environments with specific security configurations.

SaaS customization is bounded by what the vendor exposes through configuration settings, APIs, & extension frameworks. Modern SaaS platforms offer significant flexibility, but you're ultimately working within the vendor's architecture. If your requirements fall outside what the platform supports, you're either waiting for a feature request or building workarounds.

### **Maintenance & Upgrade Responsibilities**

With on-premise, every patch, upgrade, & configuration change is your team's responsibility. That's not just the application itself — it's the full stack: operating system updates, security patches, database maintenance, hardware replacements, & capacity planning.

In our experience, organizations routinely underestimate this burden when evaluating on-premise deployments.

SaaS eliminates most of this burden. The vendor handles infrastructure maintenance, security patches, & application updates. You'll typically receive new features automatically without downtime or migration effort. The tradeoff is that you lose control over update timing — and sometimes get changes you didn't ask for.

### **Scalability & Resource Flexibility**

On-premise scalability requires planning ahead. If you anticipate a traffic spike or need more capacity, you're ordering hardware, waiting for delivery, & provisioning new resources. Over-provisioning to handle peak demand means paying for capacity that sits idle most of the time. Under-provisioning means risking performance degradation when demand exceeds capacity.

SaaS scales on demand. Most platforms automatically adjust to usage, and those that don't allow you to change your subscription tier with minimal friction. For organizations with variable or unpredictable workloads, this flexibility is often the deciding factor.

### Understand On-Premise vs. SaaS

See how Sedai explains on-premise vs. SaaS in 2026 for scale, control & operational efficiency.

## **Factors to Consider When Opting for Either On-Premise or SaaS**

### **Budget**

If your organization has limited capital budget but can support ongoing operational expense, SaaS is the more accessible path. If you have the capital and want to avoid long-term subscription lock-in, on-premise may offer better total cost of ownership at scale. The break-even point varies, but for widely deployed applications with predictable usage, it's typically 3–5 years.

### **Compliance**

Regulatory requirements around data residency, sovereignty, & audit controls can make the decision for you. Industries like healthcare (HIPAA), financial services (SOX, PCI-DSS), & government (FedRAMP) often have specific requirements about where data lives & who can access it. While many SaaS vendors now offer compliance certifications & regional data hosting, some regulatory frameworks still require on-premise or private cloud deployment.

### **IT Resources**

On-premise demands a capable IT team: system administrators, network engineers, security specialists, & database administrators. If your organization doesn't have this team, building it adds significant cost and lead time to any on-premise deployment. SaaS reduces the infrastructure team requirement but introduces demand for application administrators, integration specialists, & vendor management skills.

### **Data Sensitivity**

For highly sensitive data, like classified information, trade secrets, or personally identifiable health records, the control offered by on-premise deployment is sometimes non-negotiable. You manage the encryption keys, the physical security, & the access controls end to end. SaaS vendors handle data security professionally, but you're trusting a third party with your most sensitive assets. For many organizations in 2026, the answer is a hybrid approach: sensitive workloads on-premise or in a private cloud, everything else in SaaS.

## **Why Are SaaS Solutions Replacing On-Premise Setups?**

Gartner projects worldwide software spending will grow[ 14.7% in 2026 to over $1.4 trillion](https://www.gartner.com/en/newsroom/press-releases/2026-02-03-gartner-forecasts-worldwide-it-spending-to-grow-10-point-8-percent-in-2026-totaling-6-point-15-trillion-dollars), making it the second-fastest growing IT category behind data center systems. That growth is driven overwhelmingly by SaaS and cloud-delivered software, not on-premise license sales. Five practical reasons explain why.

First, the speed advantage is real. Organizations that need to move fast can't wait months for hardware procurement and installation. SaaS lets teams deploy software in days, iterate quickly, & shift direction without sunk infrastructure costs.

Second, the maintenance burden of on-premise is unsustainable for most organizations. Keeping a growing portfolio of on-premise software patched, secured, & updated requires an IT team that keeps growing proportionally. SaaS offloads that burden to vendors who can amortize it across thousands of customers.

Third, remote and distributed work changed the accessibility equation permanently. On-premise software that requires VPN access or on-network presence creates friction for distributed teams. SaaS is accessible from anywhere by design.

Fourth, SaaS vendors invest in capabilities that most organizations can't build internally. Enterprise-grade security, disaster recovery, global availability, & AI-powered features are built into the platform cost. Replicating that level of capability on-premise requires resources that only the largest enterprises can afford.

Fifth, integration ecosystems favor SaaS. Modern SaaS platforms connect to hundreds of other tools through native integrations & APIs, creating workflows that would require significant custom development to replicate in an on-premise environment.

The shift isn't absolute, though. On-premise isn't disappearing. It's concentrating in the use cases where it genuinely makes sense: highly regulated workloads, ultra-low-latency requirements, environments where internet connectivity is unreliable, & organizations with specific data sovereignty constraints.

Whether your infrastructure runs on-prem or in SaaS, total cost of ownership is always higher than it looks. [Book a demo](https://sedai.io/demo) to see how Sedai continuously surfaces and resolves the hidden cost inefficiencies in your cloud environment.

## **How Can Sedai Help You Choose Between On-Premise vs. SaaS?**

The on-premise vs. SaaS decision determines where your software runs, but it doesn't eliminate operational complexity. It just changes the shape of it.

On-premise infrastructure internalizes complexity. Your team manages hardware, capacity planning, patching, & performance tuning directly. Cloud and SaaS infrastructure distributes complexity. The vendor handles the platform, but your team is still responsible for how applications use those resources, how costs accumulate, & how performance holds up under changing conditions.

In both cases, environments that aren't continuously optimized accumulate waste. On-premise, that waste looks like over-provisioned servers running at 15% utilization. In SaaS & cloud environments, it looks like oversized instances, idle resources, & autoscaling configurations that haven't been updated since the initial deployment.

That's where Sedai fits. For organizations running workloads in the cloud, whether they've migrated from on-premise or built cloud-native from the start, Sedai continuously analyzes application behavior & autonomously optimizes resource allocation, scaling, & cost. It doesn't just flag waste. It eliminates it, safely, by understanding each workload's performance requirements before making changes.

[KnowBe4](https://www.sedai.io/customers/knowbe4), the security awareness platform serving over 70,000 organizations globally, used Sedai to reach 98% autonomous optimization across their cloud services. The result was a 27% reduction in cloud costs, over 1,100 autonomous actions per quarter, & ROI in under five months, all without requiring their engineering team to manually review & implement each change.

Whether you've migrated from on-premise to SaaS, kept critical workloads on-prem, or ended up managing both — the operational complexity doesn't go away. It just shifts from hardware management to resource optimization. If your team is spending cycles manually rightsizing instances and tuning autoscaling policies across those environments,[ see how Sedai handles it continuously](https://sedai.io/platform).

## **FAQs**

### **What is the key difference between on-premises & SaaS?**

On-premises software runs on infrastructure your organization owns & manages. SaaS runs on the vendor's infrastructure & is accessed over the internet. The core tradeoff is control vs. convenience: on-premise gives you full control over data, security, & configuration, while SaaS eliminates infrastructure management but limits customization to what the vendor exposes.

### **What is the difference between SaaS, IaaS, & PaaS?**

SaaS delivers complete applications you use directly (like Salesforce or Slack). IaaS provides raw infrastructure, like virtual machines, storage, & networking, that you build on (like AWS EC2 or Azure VMs). PaaS provides a platform with built-in development tools, databases, & middleware so you can build & deploy applications without managing underlying infrastructure (like Heroku or Google App Engine).

### **What are the hidden costs of SaaS beyond subscription fees?**

The most common hidden costs are integration expenses (connecting SaaS tools to existing systems), data migration costs when switching vendors, training & change management for new platforms, premium tier upgrades as usage grows, & the cost of managing an expanding vendor portfolio. Organizations with 100+ SaaS subscriptions also face significant spend on license management & renewal negotiation.

---

# A Complete Guide to Hybrid Cloud Cost Management in 2026
Source: https://sedai.io/blog/hybrid-cloud-cost-management

Learn why hybrid cloud cost management is critical in 2026 and how to control spend across environments. Learn more.

## **What Is Hybrid Cloud Cost Management?**

Hybrid cloud cost management is tracking, allocating, & optimizing spend across public cloud, private cloud, & on-prem infrastructure as a single financial picture.

Simple enough on paper. But most finance teams can tell you exactly what they spend on AWS & nothing beyond that. That number is easy. It's on a dashboard, it's in a monthly report, and it's handled.

Now ask them what a single workload costs when it runs across AWS, a private cloud, & two on-prem clusters. That's where the number falls apart.

Each environment runs its own pricing model, its own metering logic, its own allocation rules. Three separate financial pictures that nobody has stitched into one.

And until you stitch them, every optimization decision is based on a partial view of the actual spend.

**In this article:**

- [Why Is Hybrid Cloud Cost Management Critical in 2026?](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=FAQs-,Why%20Is%20Hybrid%20Cloud%20Cost%20Management%20Critical%20in%202026%3F,-Hybrid%20isn%27t%20a)
- [3 Key Components of Hybrid Cloud Environments](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=not%20simplifying%20them.-,3%20Key%20Components%20of%20Hybrid%20Cloud%20Environments,-1.%20Public%20Cloud)
- [Why Do Hybrid Environments Create Blind Spots?](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=single%20cloud%20bill.-,Why%20Do%20Hybrid%20Environments%20Create%20Blind%20Spots%3F,-Inconsistent%20Cost%20Allocation)
- [Cost Drivers Most Organizations Overlook in Hybrid Cloud](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=in%20hybrid%20budgets.-,Cost%20Drivers%20Most%20Organizations%20Overlook%20in%20Hybrid%20Cloud,-Replication%20%26%20Backup%20Costs)
- [4 Strategies for Effective Hybrid Cloud Cost Management](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=organization%20operates%20under.-,4%20Strategies%20for%20Effective%20Hybrid%20Cloud%20Cost%20Management,-1.%20Unified%20Visibility)
- [Unified Cost Data Pipeline Across Hybrid Environments](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=Book%20a%20Demo-,Unified%20Cost%20Data%20Pipeline%20Across%20Hybrid%20Environments,-Cost%20Metrics%20Across)
- [When Should Hybrid Cloud Cost Management Trigger Architecture Changes?](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=about%20organizational%20discipline.-,When%20Should%20Hybrid%20Cloud%20Cost%20Management%20Trigger%20Architecture%20Changes%3F,-Cost%20data%20should)
- [Common Misconceptions About Hybrid Cloud Cost Optimization](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=migration%20or%20repatriation.-,Common%20Misconceptions%20About%20Hybrid%20Cloud%20Cost%20Optimization,-%22Hybrid%20is%20always)
- [Get Started With Sedai](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=to%20execute%20safely.-,Get%20Started%20With%20Sedai,-Every%20strategy%20in)
- [FAQs](https://sedai.io/blog/hybrid-cloud-cost-management#:~:text=Sedai%20works.-,FAQs,-Is%20a%20hybrid)

## **Why Is Hybrid Cloud Cost Management Critical in 2026?**

Hybrid isn't a transitional state anymore.[ Gartner projects 90% of enterprises will adopt hybrid cloud by 2027](https://www.gartner.com/en/newsroom/press-releases/2024-11-19-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-total-723-billion-dollars-in-2025), and most large organizations are already there. 

The problem compounds in hybrid setups. You're not just managing waste in one environment. You're managing it across environments that don't share billing formats, tagging standards, or even the same definition of "utilization." Without a unified approach, cost optimization in one environment can quietly shift costs to another.

Two trends are accelerating this in 2026. First, AI workloads are forcing organizations to run GPU-intensive training on-prem while serving inference from the cloud, creating new data movement patterns that existing cost models weren't designed for. 

Second, the wave of cloud repatriation means many enterprises are adding on-prem capacity back into environments that were already complex, not simplifying them.

Fragmented dashboards and billing chaos across AWS, Azure, and GCP compound fast. [Book a demo](https://sedai.io/demo) to see how Sedai gives you a unified optimization layer across all three.

## **3 Key Components of Hybrid Cloud Environments**

### **1. Public Cloud Resources (IaaS, PaaS, SaaS)**

These are your consumption-based services from providers like AWS, Azure, & Google Cloud. Costs are variable, usage-based, & granular. The pricing models are well-documented but complex, with hundreds of SKUs, discount tiers, & regional variations that make forecasting difficult.

### **2. Private Cloud & On-Premises Infrastructure**

These are your owned or leased environments: data centers, colocated hardware, & virtualized private cloud stacks. Costs are capital-heavy, amortized over years, & often buried in facilities budgets rather than IT line items. The real cost of running a workload on-prem is frequently understated because teams don't account for power, cooling, floor space, & staff overhead.

### **3. Integration & Orchestration Layers**

The connective tissue between environments: networking, VPNs, API gateways, container orchestration, & data replication. These are often the least visible cost center in a hybrid architecture, & they scale with complexity. Every new connection between environments adds ongoing cost that rarely shows up in a single cloud bill.

## **Why Do Hybrid Environments Create Blind Spots?**

### **Inconsistent Cost Allocation Models Across Environments**

Public cloud bills down to the API call. On-prem bills by the rack or by the VM. When a workload spans both, you're trying to reconcile two fundamentally different accounting models. We see teams consistently undercount on-prem costs because they're not applying the same granularity they expect from AWS or Azure.

### **Shadow IT & Untagged Resources in Hybrid Setups**

Untagged resources are a problem in any cloud environment. In hybrid setups, the problem multiplies. A developer spins up a test environment in a public cloud account that isn't connected to the central tagging system. 

An on-prem VM gets provisioned through a legacy workflow that predates the tagging policy. The result is spend that nobody can attribute to a team, project, or workload. 

In hybrid environments, untagged resources don't just create reporting gaps. They make it impossible to calculate the true cost of running a workload end-to-end, which undermines every downstream optimization effort.

### **The Cost of Data Gravity & Environment Switching**

Data doesn't move for free. Egress charges, replication costs, & network transfer fees accumulate every time data moves between environments. 

Organizations that store primary data on-prem but process it in the cloud (or vice versa) often discover that data movement costs rival the compute costs themselves. In our experience, this is the single most underestimated line item in hybrid budgets.

## **Cost Drivers Most Organizations Overlook in Hybrid Cloud**

### **Replication & Backup Costs**

Disaster recovery & business continuity require data replication across environments. The storage costs are visible, but the ongoing transfer costs, the snapshot management overhead, & the retention policies that nobody revisits all add up. Backup sprawl in hybrid environments is common & expensive.

### **Hybrid Integration Connectivity & VPN Costs**

Dedicated connections like AWS Direct Connect or Azure ExpressRoute carry monthly port fees, data transfer charges, & often redundancy requirements. VPN tunnels between environments consume bandwidth & require monitoring. 

These costs are committed & ongoing, but they're rarely included in workload-level cost models.

### **Multi-Environment Compliance & Audit Overhead**

Maintaining compliance across hybrid environments means running parallel audit processes, maintaining separate logging & monitoring stacks, & sometimes duplicating security tooling. 

The labor cost alone is significant, and it scales with the number of regulatory frameworks your organization operates under.

## **4 Strategies for Effective Hybrid Cloud Cost Management**

### **1. Unified Visibility & Cost Attribution**

You can't optimize what you can't see. The first step is consolidating cost data from all environments into a single view with consistent tagging, allocation rules, & reporting cadence. This means connecting public cloud billing APIs, on-prem metering tools, & license management systems into one pipeline. It's foundational work, and most organizations underinvest in it.

### **2. Continuous Rightsizing & Resource Optimization**

Manual rightsizing doesn't scale across hybrid environments. By the time a team finishes reviewing one environment, the other has already drifted. The approach that works is [autonomous optimization](https://www.sedai.io/blog/cloud-optimization-the-ultimate-guide-for-engineers) — systems that learn each workload's actual behavior and adjust resources based on real application data, not static rules or predefined thresholds.

This includes matching instance families to actual workload profiles, adjusting autoscaling thresholds based on real traffic patterns rather than initial estimates, & identifying resources that have been idle for more than a defined period. Critically, every change needs to account for the workload's performance requirements. Aggressive rightsizing that ignores latency constraints or availability SLOs creates incidents, not savings. 

In our experience, the first optimization pass typically surfaces 20-30% in recoverable waste.

### **3. Chargeback & Showback for Accountability**

When teams don't see the cost of their resource consumption, they don't manage it. Chargeback (billing teams directly) or showback (reporting costs without billing) creates accountability. In hybrid setups, this requires consistent cost allocation across environments, which is why unified visibility comes first.

Start with showback. It's lower friction than chargeback & gives teams visibility without the political overhead of internal billing disputes. Once teams are used to seeing their costs, the transition to chargeback becomes a natural next step.

### **4. Governance Policies & Cost Guardrails**

Proactive cost control beats reactive cleanup. Set budget thresholds, enforce tagging at provisioning, require approval workflows for high-cost resource types, & automatically flag resources that exceed usage baselines. A strong[ cloud cost optimization strategy](https://www.sedai.io/blog/cloud-cost-optimization-strategies) builds these guardrails into the provisioning process itself.

### Understand Hybrid Cloud Cost Management

See how Sedai explains hybrid cloud cost management in 2026 for better visibility, cost control & optimized performance.

## **Unified Cost Data Pipeline Across Hybrid Environments**

### **Cost Metrics Across Public & Private Cloud**

Public cloud cost metrics (cost per compute hour, cost per GB stored, cost per API call) are well-defined. On-prem equivalents are not. Building a unified pipeline means defining comparable metrics across environments: cost per workload, cost per transaction, or cost per user, normalized across both billing models.

### **Real-Time Cost Streaming vs. Batch Reporting**

Monthly cost reports tell you what already happened. By the time you act, the waste has compounded. Real-time cost streaming, where cost data updates continuously alongside performance & utilization data, lets teams catch anomalies within hours instead of weeks. Monthly reports tell you what went wrong. Real-time streaming lets you prevent it from compounding.

### **Creating a Single Source of Truth for Hybrid Cloud Spend**

The goal is one dashboard, one set of allocation rules, & one reporting cadence across all environments. We've seen organizations run three or four separate cost tools, one per environment, each with different tagging standards & different reporting timelines. The result is nobody trusts any of the numbers. A single source of truth is less about tooling and more about organizational discipline.

Unified visibility across on-premises and public cloud is only valuable if it leads to action. [Book a demo](https://sedai.io/demo) to see how Sedai delivers a single autonomous optimization layer across hybrid environments.

## **When Should Hybrid Cloud Cost Management Trigger Architecture Changes?**

Cost data should inform architecture decisions, not just budgeting. If a workload consistently costs more in the cloud than on-prem (or vice versa), that's a signal to evaluate whether it's in the right environment.

Triggers to watch for: workloads where data transfer costs exceed 30% of total cost, steady-state workloads running on pay-as-you-go pricing for more than six months, & environments where compliance overhead is duplicating labor across teams. When[ managing multiple cloud environments](https://www.sedai.io/blog/multi-cloud-management-tools), the architecture should follow the economics, not the other way around.

A practical approach: run a quarterly cost-per-workload review across environments. Compare the fully loaded cost of each workload (compute, storage, networking, staff overhead, & compliance) in its current environment against what it would cost in the alternative. If the delta exceeds 20% consistently over two quarters, that workload is a candidate for migration or repatriation.

## **Common Misconceptions About Hybrid Cloud Cost Optimization**

**"Hybrid is always more expensive than going all-in on one environment."** Hybrid adds coordination costs, but it also lets you place each workload where it runs most cost-efficiently. The overhead is real, but it's usually smaller than the savings from right-placement.

**"If we tag everything, we'll have cost visibility."** Tagging is necessary but insufficient. Tags without consistent allocation rules, reporting cadence, & cross-environment normalization just produce data, not insight.

**"FinOps solves this."** FinOps is a practice, not a fix. Without automation to act on what FinOps surfaces, you end up with great dashboards & no improvement. We've seen FinOps teams produce monthly reports that nobody acts on because the manual effort to implement changes exceeds the team's capacity.

FinOps programs typically stall at the insight stage — surfacing waste is straightforward, but eliminating it requires coordinated changes across providers that most teams don't have the bandwidth to execute safely.

## **Get Started With Sedai**

Every strategy in this guide depends on one thing: acting on what cost analysis surfaces, continuously and without breaking production. In hybrid environments, that's where most programs stall — the coordination required across providers exceeds what manual processes can sustain.

Sedai approaches this differently. The platform learns how each workload actually behaves before making any change in production. That's how Palo Alto Networks runs over 89,000 production changes through Sedai with zero incidents — their cloud environments stay optimized across providers without engineers manually re-tuning after every release or migration.

If your team is spending more time reconciling hybrid cloud bills than building features,[ see how Sedai works](https://sedai.io/platform).

## **FAQs**

### **Is a hybrid cloud cost-effective?**

It can be, when workloads are placed in the right environment for their cost profile. Steady-state workloads often cost less on-prem, while variable workloads benefit from cloud elasticity. The key is continuous evaluation, not a one-time placement decision.

### **What are the disadvantages of a hybrid cloud?**

Operational complexity is the biggest drawback. Managing cost visibility, security, compliance, & performance across multiple environments requires more tooling, more coordination, & more specialized skills than a single-environment approach.

### **What is an example of a hybrid cloud?**

A financial services company that keeps customer transaction data on-prem for regulatory compliance while running its analytics & machine learning pipelines in AWS. The on-prem systems handle latency-sensitive processing, while the cloud handles burst compute for model training.

---

# Sedai Raises $15M Funding for Autonomous Cloud Management
Source: https://sedai.io/blog/sedai-raises-15m-funding-for-autonomous-cloud-management

Sedai secured $15M+ in Series A funding led by Norwest Venture Partners spearheading SRE's future. Join the autonomous revolution!

[Sedai,](http://www.sedai.io) the first autonomous cloud management company, today announced that it has raised $15 million in Series A funding led by Norwest Venture Partners with participation from Sierra Ventures and Uncorrelated Ventures. The round follows a seed led by Sierra Ventures and Uncorrelated Ventures and brings the total funding to $18.8 million. Sedai will use the funding for growth and today [announced the opening of an Engineering Center in Kerala, India](http://www.sedai.io/post/sedai-to-open-new-r-d-center-in-india). The company is growing its development team to increase support for all cloud platforms and broaden its ecosystem. 

Enterprises are under immense pressure to push new features fast to remain competitive, which means companies need to scale quickly and release more often. With today's cloud infrastructures, it is no longer realistic for organizations and site reliability engineer (SRE) teams to innovate while keeping track of hundreds of microservices and multiple dependencies. 

Cloud costs and performance do not manage themselves. [Book a demo](https://sedai.io/demo) to see how Sedai takes continuous autonomous action across your entire environment.

“To effectively manage scale, companies are rethinking the “shift left” “shift right” operations and automation that are slowing them down,” said Suresh Mathew, Founder & CEO. “Sedai delivers [SREs a platform](https://www.sedai.io/roles/site-reliability-engineering) that can act independently on their behalf, learn from them, and carefully measure the efficacy and continuously fix and improve right in **production.”**

“We built Sedai to let every SRE and developer experience the same benefits of an autonomous system that has previously only been accessible to the large enterprise technology companies,” said Benjamin Thomas, Co-Founder, Sedai. “With Sedai, companies no longer need to be burning out their SREs and can focus on innovating more with a lot less stress.”

### Autonomous Cloud Management Platform Built on Investor-Validated Momentum Toward Self-Optimizing Infrastructure

See how Sedai's $15M-backed platform continuously rightsizes workloads and applies cloud optimizations without the manual intervention that reporting-only tools still require.

##### **The Shift from Automated to Autonomous**‍

Sedai is the first company to deliver an autonomous cloud management system into the hands of every organization. SREs can connect Sedai to their cloud in minutes, and the platform automatically discovers resources and intelligently analyzes traffic and performance metrics. Sedai then continuously manages production environments with proactive actions—without manual thresholds or human intervention. 

Sedai’s machine learning model detects symptoms early and prevents issues ahead of time. Its intelligent, context-aware system means that it can safely run in production and proactively improve the overall experience. Because Sedai acts as an [intelligent co-pilot to the SRE](https://www.sedai.io/use-cases/ops-productivity), continuously monitoring and managing microservices, SREs can now effortlessly achieve higher levels of availability and reduce cloud costs.

“Sedai has created the tool every SRE dreams of—autonomous cloud management to proactively and safely address issues in production. The improvements that companies are seeing in reliability, uptime, and efficiency show how beneficial this technology is,” said Matt Howard, general partner at Norwest Venture Partners. “Suresh and Benji proved this approach at scale while at PayPal, and we are excited to partner with them in bringing it to the market.” “Global competitiveness by businesses of all sizes is being defined by the success of their digital strategies in this coming decade, which is driving strong growth in the $40B APM and Digital Transformation market,” said Tim Guleri, Managing Partner at Sierra Ventures. “Suresh and Benji are the dream team, coming from PayPal and building an autonomous application management company that has exploded out of the gate. With 60% of all large enterprises projected to adopt autonomous technologies by 2024, customers like Fabric have already gotten on board with Sedai to stay competitive and scale. We are excited to back this talented team and look forward to the growth ahead.”

Investor confidence reflects the market shift from cost visibility to autonomous action. [Book a demo](https://sedai.io/demo) to see why enterprise teams are choosing Sedai to make their cloud environments self-optimizing.

##### **Sedai Customers** ‍

Several businesses are already using the [Sedai platform](https://www.sedai.io/platform) to [manage their cloud environments](https://www.sedai.io/schedule-demo) and production applications ranging from serverless start-ups to larger enterprises going through a digital transformation and moving to modern cloud environments.*‍*

> *"Sedai watches over our Lambdas and proactively makes configuration adjustments depending on what we need to do—whether it’s to minimize the latency or minimize our cloud costs."Kenneth NguyenTasq, Co-Founder*

‍

> *"Sedai helped us scale from having only one SRE supporting 2-3 customers on [our commerce platform](https://www.sedai.io/industries/ecommerce) to now we can support 20 customers."* Prakash MuppiralaFabric, Executive Vice President

Join the autonomous movement and start using [Sedai for free](https://setup.sedai.app/).

### Frequently Asked Questions

The funding supports Sedai's mission to advance autonomous cloud management, expanding its platform that optimizes cost, performance, and availability without manual intervention. It fuels product development and market growth.

#### Why did Sedai raise $15M in funding?

Sedai is an autonomous cloud management platform that uses AI to continuously optimize cloud cost, performance, and reliability across compute, serverless, containers, and databases, acting safely in production.

#### What does Sedai do?

It is software that senses live application behavior and independently takes optimization and remediation actions, going beyond rule-based automation to adapt as conditions change.

#### What is autonomous cloud management?

Most tools only report spend or recommend changes, while Sedai autonomously acts to optimize resources in production, turning insight into outcomes without manual effort.

#### How is Sedai different from traditional cloud cost tools?

---

# How to Survive the Death of SaaS
Source: https://sedai.io/blog/how-to-survive-death-of-saas

Pricing is breaking. Infrastructure costs are unpredictable. And most SaaS leaders think they're safe. Here's why they're wrong.

Everyone in tech is clamoring about the death of SaaS. And I get it: AI is here to stay, and it’s understandable to question whether or not this seismic shift will completely change what we know as SaaS.

But through all the thought pieces on *why* SaaS is dying, I have yet to see anyone truly tackle what it takes for tech leaders to navigate it. So let me be direct: SaaS isn't dying. But AI *is* destabilizing it faster than most leaders want to admit.

I've been in this industry long enough to remember when competitive moats were measured in years. Now, “move fast or die” has gone from the years-long competitive process to mere months. And markets noticed.

In early February, [Forbes wrote](https://www.forbes.com/sites/donmuir/2026/02/04/300-billion-evaporated-the-saaspocalypse-has-begun/) that in one day, about $300 billion of market value “evaporated” from the SaaS market. Even betting *against* SaaS has become lucrative, with short sellers making over $20 billion betting against legacy SaaS since the start of 2026.

And it's not just the markets. I recently read a post from a fellow engineer about how he wrote an anomaly detection tool to review cloud costs using Claude. That’s an entire cloud cost observability category wiped out by one guy and a chatbot.

Most cost visibility tools show you the problem. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking autonomous action based on your actual workload behavior.

So how do we as SaaS leaders adapt to this new reality? It starts by understanding how the model broke and why our survival instincts are wrong.

## How the SaaS Model Broke

In the cloud space, you can see exactly where the instability hits.

When we started Sedai, optimization cycles were slower. Customers evaluated savings over quarters. Infrastructure behavior was relatively predictable. Workloads scaled within guardrails, and engineering teams could keep up.

Over the past year, that changed dramatically.

AI workloads introduced burst patterns we haven’t seen before, GPU costs that spike in hours, & traffic patterns that shift daily. Now, customers expect both savings & zero performance regression simultaneously & instantaneously.

Revenue conversations shifted, too. CFOs now scrutinize marginal compute cost per feature. Engineering leaders are accountable not just for uptime, but for cost per inference.

The volatility is no longer quarterly. It’s real time. And most SaaS leaders are responding to it in the wrong way.

## Why SaaS Companies' Survival Instincts Are Wrong

There’s a lot of hand-waving from SaaS leaders who assume they’re insulated from the instability because customers can’t easily rip them out.

But as [Jason Lemkin pointed out in his article about the death of SaaS](https://www.saastr.com/is-saas-dead-no-but-one-thing-is-clear-its-unstable/?utm_source=tldrfounders), “If you’re a system of record and you don’t own the AI agents that make your platform thrive… You’ll become the database underneath someone else’s product.”

"Owning the AI layer" isn't bolting on a chatbot or adding AI features to your roadmap. It means engineering autonomy into the parts of your business that AI makes most volatile, and grounding that autonomy in your system of record. 

At Sedai, that meant rethinking how we look at infrastructure, pricing, & people. When we center these things around autonomy, we can stop reacting to volatility and start absorbing it.

### Ready to adapt to the next era of cloud software?

Book a Sedai demo to automate cloud optimization, improve operational efficiency, and stay ahead of evolving technology trends.

### Pricing Strategy

Pricing is where the old SaaS model breaks first. With traditional SaaS, we saw:

- Seat-based pricing create artificial stability 
- Revenue scale with headcount 
- Ops teams gradually optimize infrastructure costs
- Margins improve slowly over time

But in the AI era:

- Revenue scales with usage 
- Usage directly drives compute
- Compute is volatile

Every AI feature has a marginal cost. Every inference, retrain, & spike in traffic carries real infrastructure consequences. If you don’t understand cost per action in real time, pricing becomes guesswork.

The most dangerous scenario is subtle: You aggressively price AI capabilities to win distribution. You see adoption grow & usage spike, but gross margin erodes quietly beneath you. By the time finance flags it, it’s systemic.

At Sedai, this is where we found autonomous optimization becomes non-negotiable. If revenue is scaling with usage, your infrastructure must adapt at the same speed.

AI-native SaaS companies must design pricing alongside a system that relentlessly optimizes cloud cost & performance in real time. A continuously learning control layer that:

- Detects volatility
- Adjusts resources dynamically
- Validates impact
- Protects margin automatically

When pricing becomes disconnected from your infrastructure’s reality, margin erosion becomes a guarantee.

### Infrastructure Efficiency

Infrastructure efficiency is now strategic survival. AI workloads are bursty, nonlinear, retrain unpredictably, & often require GPU resources that cost 10x what traditional compute costs. 

When your infrastructure can't absorb that volatility, you see costs spike & performance degrade.​​ The complexity is too high and changes happen too fast for human operators to manage manually anymore.

To become durable, companies must [embed autonomous optimization directly into their infrastructure stack](https://sedai.io/blog/guardrails-as-code). 

This doesn’t mean implementing scripts, static automation, or alerts only humans can act on. Instead, it means using systems that continuously observe real behavior, decide on optimizations, test changes safely, & learn from outcomes. 

In the AI era, infrastructure efficiency goes beyond cost savings and allows your teams to engineer stability into your product that the market no longer guarantees.

### People Optimization & Upskilling

As an engineer myself, the engineers I see fighting AI adoption, or simply moving too slowly on it, are going to become irrelevant. 

Not eventually. Soon.

That's not a comfortable thing to say, but I think it's a disservice to pretend otherwise.

With SaaS companies using AI to automate engineering tasks, the instinct is to cut headcount. But that’s the wrong move; tech leaders should see this as an opportunity to upskill the engineering teams, not question the right number of engineers.

The companies that will stay competitive will be the ones that enable their engineers to direct, validate, & accelerate what AI produces. 

At Sedai, this was easier than it might be elsewhere: AI adoption is in our engineering DNA. Our team didn't need convincing. But I know that's not the norm.

I've been having a lot of [conversations on my podcast with engineering leaders](https://www.youtube.com/watch?v=Tpa0GHc0eFE) about how other engineering teams are adopting AI, and a few patterns keep coming up.

For most teams, the practical entry point is documentation. It's low risk, immediately valuable, & removes one of the most avoided tasks in engineering. 

From there, move into testing. Let AI generate test cases, validate edge cases, & catch regressions. Once engineers see how much faster they ship with AI handling that layer, adoption moves naturally into code generation & review.

The progression matters. Each of these steps builds trust in what AI produces before handing it more responsibility. By the time engineers are using AI to generate & review code, they're not replacing their judgment, they're just applying it at a higher level.

Headcount and tooling can only get you so far. The real upskilling happens when engineers focus on orchestrating what AI produces rather than just prompting it. 

Organizations with clear visibility into their full technology spend — cloud and SaaS — are better positioned to make architecture decisions that reduce total cost of ownership. [Book a demo](https://sedai.io/demo) to see how Sedai contributes to that picture.

## Conclusion

We need to stop pretending what used to work in traditional SaaS can work in an AI-native world. The SaaS companies that survive the shift will be built on autonomous systems that optimize, decide, & adapt faster than any human operator can. 

Sedai is built for this, because we've been building toward it from the start. Every decision we've made, from how we think about pricing, approach infrastructure efficiency, and build our teams comes down to one thing: [you have to engineer autonomy into the foundation](https://sedai.io/platform).

---

# IaaS vs. PaaS vs. SaaS
Source: https://sedai.io/blog/iaas-vs-paas-vs-saas

A practical breakdown of IaaS vs PaaS vs SaaS with real-world examples. Explore now.

If you have experience with cloud computing, you have likely encountered explanations of IaaS, PaaS, and SaaS many times, often with diagrams or analogies that oversimplify the reality.

In practice, confusion arises not from definitions, but from understanding when each model is appropriate, the tradeoffs involved, & why most systems use a combination of all three.

This article examines IaaS, PaaS, & SaaS from the perspective of engineers & platform leaders in 2026, focusing on: 

- Control
- Responsibility, speed, and long-term operational impact.

## Table of Content

- [What Is IaaS?](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=What%20Is%20IaaS%3F-,What%20Is%20IaaS%3F,-Infrastructure%20as%20a)
- [What Is PaaS?](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=provider%E2%80%99s%20data%20center.-,What%20Is%20PaaS%3F,-Platform%20as%20a)
- [What Is SaaS?](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=and%20customization%20options.-,What%20Is%20SaaS%3F,-Software%20as%20a)
- [IaaS, PaaS, and SaaS Key Differences](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=IaaS%2C%20PaaS%2C%20and%20SaaS%20Key%20Differences)
- [When to Choose IaaS, PaaS, or SaaS](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=When%20to%20Choose%20IaaS%2C%20PaaS%2C%20or%20SaaS)
- [IaaS vs PaaS vs SaaS Examples by Industry](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=IaaS%20vs%20PaaS%20vs%20SaaS%20Examples%20by%20Industry)
- [Why Modern Architecture Takes a Hybrid Approach?](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=Why%20Modern%20Architecture%20Takes%20a%20Hybrid%20Approach%3F)
- [Conclusion](https://sedai.io/blog/iaas-vs-paas-vs-saas#:~:text=any%20other%20way.-,Conclusion,-The%20debate%20around)

## What Is IaaS?

**Infrastructure as a Service (IaaS)** delivers raw computing resources, including virtual machines, storage, networking, and load balancers. It is comparable to renting a data center that you manage through APIs.

With IaaS, you manage:

- Operating systems
- Runtime environments
- Scaling logic
- Security configurations
- Application code

The cloud provider manages physical hardware and basic virtualization, while you are responsible for all layers above.

**Why teams choose IaaS:**

- Maximum control over the stack
- Ability to support legacy or highly customized workloads
- Flexibility to run almost anything

**The tradeoff:**
IaaS offers flexibility, but also increases your responsibilities. Tasks such as patching, scaling, monitoring, and ensuring reliability remain your responsibility, even when they occur in a provider’s data center.

Most cost visibility tools show you the problem. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking autonomous action based on your actual workload behavior.

## What Is PaaS?

**Platform as a Service (PaaS) operates at a higher level. Rather than** managing servers and operating systems, you deploy applications to a managed platform.

With PaaS, the provider typically handles:

- OS patching
- Runtime management
- Auto-scaling
- Load balancing
- Some security defaults

You focus on:

- Application code
- Configuration
- Data models

PaaS is most effective when your application aligns with the platform’s supported patterns and requirements.

**Why teams choose PaaS:**

- Faster deployment
- Less operational overhead
- Smaller platform teams

**The tradeoff:**
You give up some control. You relinquish some control and are limited by the platform’s supported features, scaling methods, and customization options.

## What Is SaaS?

**Software as a Service (SaaS)** is the highest level of abstraction. You don’t manage infrastructure or platforms—you just use the software.

Email, CRM, analytics tools, and collaboration platforms—all fall under SaaS.

With SaaS, the provider manages:

- Infrastructure
- Platform
- Application logic
- Updates and security

You manage:

- Configuration
- Users
- Data usage

**Why teams choose SaaS:**

- Fastest time to value
- No infrastructure or platform work
- Predictable pricing

**The tradeoff:**
Control is very limited. Your processes must adapt to the software, rather than customizing the software to fit your processes.

### IaaS, PaaS & SaaS Tradeoffs That Shape Modern Cloud Architecture

See how Sedai helps engineering teams manage hybrid cloud environments across IaaS, PaaS, and SaaS by continuously optimizing cost, reliability, and operational efficiency while balancing control, speed, and simplicity.

## IaaS, PaaS, and SaaS Key Differences

Here’s where **IaaS vs. PaaS vs. SaaS** becomes clearer when you compare them side by side.

| Dimension | IaaS | PaaS | SaaS |
| --- | --- | --- | --- |
| Management responsibility | You manage OS, runtime, and apps | You manage apps | Provider manages everything |
| Control level | Highest | Medium | Lowest |
| Customization | Full | Limited to the platform | minimal |
| Deployment speed | Slowest | Faster | Fastest |
| Cost model | Usage-based infra | Usage + Platform fee | Subscription-based |
| Scalability | Manual or scripted | Built-in | Fully managed |
| Maintenance burden | High | Medium | Low |
| Skill requirement | Strong infra & ops skills | App-focused | Minimal technical skills |

This table explains that debates about which model is superior often overlook that each optimizes for different constraints.

## When to Choose IaaS, PaaS, or SaaS

### **Choose IaaS when:**

- You need deep control over networking, OS, or runtime behavior
- You’re running legacy workloads or custom stacks
- Compliance or security requirements demand fine-grained configuration

### **Choose PaaS when:**

- You want to move fast without building a large platform team
- Your application fits common patterns
- You’re okay trading some flexibility for speed and simplicity

### **Choose SaaS when:**

- The problem is not core to your competitive differentiation
- You want fast deployment and minimal operational effort
- Customization needs are limited

In real organizations, these choices are rarely binary.

## IaaS vs PaaS vs SaaS Examples by Industry

The difference between **IaaS, PaaS, and SaaS** becomes much clearer when you look at how each model is actually used across industries. In practice, most organizations don’t choose just one; they mix and match based on workload criticality, compliance needs, and the level of control their teams want over the stack.

Across industries, the **IaaS vs PaaS vs SaaS examples** follow a consistent pattern:

- **IaaS** → Maximum control, custom architectures, compliance-heavy workloads
- **PaaS** → Faster development, less operational overhead
- **SaaS** → Ready-to-use business applications with minimal IT involvement

Below are **realistic, industry-specific examples** showing how IaaS, PaaS, and SaaS are applied in the real world.

### Enterprise Application Development

Large enterprises often use:

- **IaaS** for legacy ERP systems or tightly controlled back-end services
- **PaaS** for new internal tools and APIs
- **SaaS** for collaboration, HR, CRM, and finance

This mix allows teams to modernize gradually without rewriting everything at once.

### Data Analytics & Machine Learning Workloads

Analytics stacks often combine:

- **IaaS** for custom compute-intensive workloads
- **PaaS** for managed data pipelines and model deployment
- **SaaS** for BI, dashboards, and reporting

The data layer tends to stay closer to IaaS or PaaS, while consumption moves toward SaaS.

### E-Commerce & Customer-Facing Applications

A typical setup might look like:

- **PaaS** for customer-facing applications and APIs
- **IaaS** for specialized services like search or recommendations
- **SaaS** for payments, marketing automation, and customer support

Here, speed and scalability matter more than low-level control.

### Regulated Industries & Compliance-Driven Environments

Heavily regulated industries often lean toward:

- **IaaS** for sensitive workloads requiring strict control
- **PaaS** selectively, where compliance certifications exist
- **SaaS** only for non-core or well-regulated functions

IaaS, PaaS, and SaaS each carry hidden cost tradeoffs that compound as usage scales. [Book a demo](https://sedai.io/demo) to see how Sedai continuously optimizes your cloud infrastructure costs regardless of which service model your team has adopted.

## Why Modern Architecture Takes a Hybrid Approach?

Very few modern systems are purely IaaS, PaaS, or SaaS.

Most successful architectures are **hybrid by design**.

Why?

Because different parts of the system have different requirements:

- Core systems might need control (IaaS)
- Customer-facing services might need speed (PaaS)
- Business functions might need simplicity (SaaS)

Trying to force everything into one model usually creates friction somewhere else.

In 2026, the main challenge is not just selecting the best model, but managing all three to avoid operational problems. The real strength of the cloud is its programmable infrastructure, which can handle tasks autonomously.

This is the gap Sedai closes.

Instead of asking teams to react to a never-ending stream of alerts or "clever prompts," Sedai treats the cloud as programmable infrastructure. Our platform operates across the IaaS, PaaS, and SaaS layers to handle the "boring" but critical execution; automatically adjusting cost, and reliability in real-time.

We believe in separating human reasoning from machine execution. By letting an autonomous system handle the systematic, unglamorous work of infrastructure management, your engineers are finally free to focus on what actually drives value: product innovation and solving the next big problem.

Once you’ve seen your infrastructure manage itself, it’s hard to justify doing it any other way.

## Conclusion

The debate around **IaaS vs. PaaS vs. SaaS** isn’t about which one wins. It’s about understanding tradeoffs.

IaaS gives you control.
PaaS gives you speed.
SaaS gives you simplicity.

Modern systems use all three - intentionally.

Successful teams in 2026 will not be those who commit to a single model indefinitely, but those who adapt their approach as requirements evolve.

## FAQ

### Can you use IaaS, PaaS, and SaaS together in the same architecture?

Yes—and most organizations do. Different layers of the system benefit from different levels of abstraction.

### Does PaaS lock you into a specific cloud provider?

It can, depending on how tightly you couple your application to platform-specific services. Designing for portability helps, but tradeoffs are inevitable.

### Can I migrate from IaaS to PaaS or vice versa?

Yes. Many teams start on IaaS for flexibility and move to PaaS as patterns stabilize or move back when they hit platform limits.

### Which service model offers the most control over infrastructure?

IaaS provides the highest level of control, but also the highest operational responsibility.

---

# Cloud Computing Benefits in 2026
Source: https://sedai.io/blog/benefits-of-cloud-computing

A comprehensive guide to cloud computing benefits beyond infrastructure and hardware. Explore now.

For many years, cloud computing was primarily marketed as a cost-saving solution.

“Stop buying servers. Stop overprovisioning. Pay only for what you use.”

This message resonated when most companies operated physical data centers & faced hardware limitations. However, in 2026, this perspective is incomplete & can be misleading.

Today, the primary [benefits of cloud computing](https://sedai.io/blog/enterprise-cloud-cost-optimization-solutions-with-roi) are less about cost savings & more about enabling teams to build, deploy, recover, & adapt efficiently. The cloud has become the standard execution platform for modern businesses, not just because of cost, but because it eliminates significant operational friction.

Most cost visibility tools show you the problem. [Book a demo](https://sedai.io/demo) to see how Sedai fixes it, taking autonomous action based on your actual workload behavior.

This article outlines the financial, operational, productivity, & strategic advantages of cloud computing in 2026, from the perspective of practitioners managing these systems. We cover:

- [What Is Cloud Computing?](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=Conclusion-,What%20Is%20Cloud%20Computing%3F,-At%20a%20basic)
- [Financial Benefits](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=computing%E2%80%99s%20key%20benefits.-,Financial%20Benefits,-Financially%2C%20cloud%20computing) 
- [Operational Benefits](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=for%20cloud%20adoption.-,Operational%20Benefits,-Beyond%20cost%2C%20the)
- [Productivity Benefits](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=can%20achieve%20independently.-,Productivity%20Benefits,-When%20infrastructure%20is)
- [Strategic Benefits](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=over%20managing%20infrastructure.-,Strategic%20Benefits,-Over%20time%2C%20these)
- [How Can You Quantify Cloud Computing Benefits?](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=of%20cloud%20computing.-,How%20Can%20You%20Quantify%20Cloud%20Computing%20Benefits%3F,-Not%20all%20cloud)
- [How to Maximize Cloud Computing Benefits](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=just%20reduced%20spend.-,How%20to%20Maximize%20Cloud%20Computing%20Benefits,-Moving%20workloads%20to)
- [Conclusion](https://sedai.io/blog/benefits-of-cloud-computing#:~:text=without%20breaking%20production.-,Conclusion,-The%20benefits%20of)

## What Is Cloud Computing?

At a basic level, cloud computing means consuming compute, storage, networking, databases, & software over the internet instead of owning physical infrastructure.

You don’t buy servers, rack hardware, or plan capacity years in advance. Instead, you request resources, usually via an API, & return them when you’re done.

However, this definition does not capture the full value.

In practice, cloud computing changes how teams approach constraints. Infrastructure shifts from a fixed asset to an elastic resource that can be adjusted in real time. This transition from static to programmable capacity unlocks many of cloud computing’s key benefits.

## Financial Benefits 

Financially, cloud computing fundamentally changes how organizations manage costs, risks, & long-term investments, aligning spending more closely with business outcomes.

### Pay-as-You-Go Pricing & Cost Predictability

A key advantage of cloud computing is its pay-as-you-go pricing model. Rather than making significant upfront investments, organizations pay for resources as they are consumed.

In 2026, the significance lies less in cost reduction and more in shifting financial risk. Organizations no longer need to predict demand years in advance. [Resources can be scaled up during traffic spikes](https://sedai.io/blog/cloud-native-performance-optimization-strategies#:~:text=2.%20Predictive%20Scaling) and scaled down if projects pause.

This level of flexibility is difficult to achieve with on-premises infrastructure.

Cloud computing also enables more accurate cost attribution to specific business activities. Usage can be linked to teams, applications, or individual features, facilitating clearer discussions between engineering & finance.

### Eliminate Hardware Refresh Cycles

Managing on-premises infrastructure involves regular hardware refresh cycles. Over time, hardware performance declines, vendors recommend upgrades, & budgets must be renegotiated.

The cloud removes that entire category of work.

While hardware refreshes still occur, the provider manages them continuously without disruption. Teams benefit from updated hardware & improved performance without undertaking large-scale replacement projects.

This benefit may not be immediately visible in financial reports, but it saves significant time and reduces cognitive burden.

### Reduce IT Staffing & Operational Overhead

On-premises environments require personnel to manage patching, backups, failover, capacity planning, & physical security. Cloud platforms handle much of this automatically.

This does not eliminate the need for skilled engineers; it shifts their focus. Less time is spent on maintenance, & more on enhancing reliability, performance, & product functionality.

For many organizations, this shift alone provides sufficient justification for cloud adoption.

## Operational Benefits

Beyond cost, the true impact of cloud computing is evident in daily operations, particularly in how quickly systems can be deployed, scaled, & maintained for resilience.

### Instant Resource Provisioning & Elasticity

Rapid provisioning is one of the most tangible benefits of cloud computing.

What used to take weeks of procurement, approvals, & setup can now be completed in minutes. Entire environments can be created, tested, & decommissioned within a single day. 

But it can go even further. [Systems can scale automatically](https://sedai.io/blog/strategies-to-improve-cloud-efficiency-and-optimize-resource-allocation) based on demand instead of being sized for worst-case scenarios. That means fewer outages during spikes & less waste during quiet periods.

In 2026, elasticity is a fundamental requirement.

### Global Deployment

Cloud computing makes global deployment standard practice.

Applications can be deployed across multiple regions with minimal added complexity. Latency is reduced, disaster recovery becomes feasible, & regulatory requirements for data residency are easier to meet.

Tasks that previously required multiple data centers & complex networking are now largely managed through configuration. For global businesses or any company planning to be one, this is a major operational advantage.

### Built-In Redundancy & High Availability

Achieving high availability was previously costly & complex, requiring duplicate hardware, detailed failover processes, & ongoing testing.

In cloud environments, redundancy is built in. Services are distributed across availability zones, failover mechanisms are integrated, & backups are automated.

While outages can still occur, the baseline resilience provided by cloud platforms is significantly higher than what most organizations can achieve independently.

### Understand Cloud Computing Benefits

See how Sedai breaks down cloud computing benefits in 2026 for scale, speed & cost.

## Productivity Benefits

When infrastructure is no longer a bottleneck, the most significant improvements are seen in team productivity, including how quickly teams learn, build, & adapt.

### Reduce Innovation Cycle Time

One of the often-overlooked benefits of cloud computing is its ability to accelerate learning. Teams can prototype quickly, test ideas, & iterate without waiting on infrastructure. If something doesn’t work, the cost is time, not sunk capital.

This shift encourages more experimentation, earlier validation of assumptions, & faster elimination of ineffective ideas.

Over time, these effects lead to faster innovation cycles throughout the organization.

### Eliminate Procurement & Setup Friction

Traditional infrastructure often introduces delays when teams are most motivated to move quickly, requiring approvals, purchase orders, & extended lead times.

Cloud computing removes much of this friction. Resources are available immediately, environments are reproducible, & setup is managed through code rather than manual coordination.

This might sound small, but friction kills momentum & momentum is hard to regain once lost.

### Freeing Engineers from Toil

By 2026, engineering time will be among the most valuable resources for organizations.

Cloud platforms offload significant undifferentiated tasks such as: 

- Database operations 
- Scaling
- Monitoring 
- Backups 

This [reduces cognitive load on teams](https://sedai.io/blog/cloud-architecture-optimization-business-growth) and enables engineers to focus on customer & business challenges.

This also supports hiring & retention, as engineers typically prefer building systems over managing infrastructure.

## Strategic Benefits

Over time, these financial, operational, & productivity gains create a structural advantage in how organizations compete & grow.

### Speeding Time to Market

Speed is a competitive advantage, & cloud computing is one of the most effective ways to achieve it without waiting for infrastructure. Companies can enter new regions without physical build-out. Features can be tested with limited exposure.

Companies that [move faster learn faster](https://sedai.io/blog/key-benefits-cloud-automation), & that advantage compounds over time.

### Lowers the Potential Cost of Failure

Most initiatives fail, but cloud computing reduces the cost of failure.

Projects can begin on a small scale, be validated quickly, & be discontinued without unused hardware or long-term commitments. This encourages organizations to take calculated risks.

This transition from avoiding failure to managing it is a key strategic advantage of cloud computing.

## How Can You Quantify Cloud Computing Benefits?

Not all cloud benefits show up as [direct cost savings](https://sedai.io/blog/cloud-cost-optimization-strategies-practices).

In practice, organizations measure cloud value through operational & productivity metrics such as:

- Time to provision environments
- Deployment frequency
- Mean time to recovery (MTTR)
- Infrastructure cost per unit of revenue or traffic
- Percentage of engineering time spent on platform vs product work

The biggest gains usually show up in speed, reliability, & throughput — not just reduced spend.

## How to Maximize Cloud Computing Benefits

Moving workloads to the cloud is not enough.

Organizations see the strongest results when they:

- Design systems for elasticity instead of lifting & shifting rigid architectures
- [Invest in automation](https://sedai.io/blog/cloud-workload-automation-guide) & infrastructure as code
- Treat cloud spend as an engineering signal, not just a finance problem
- Continuously[ tune performance & reliability](https://sedai.io/blog/cloud-performance-optimization-best-practices), not just cost

At Sedai, we've seen that the biggest benefits for teams managing large, distributed cloud environments often show up after migration. These advantages come when organizations move past static infrastructure & build systems that can keep adapting.

In 2026, the main value of the cloud won't be just elasticity or global reach. Instead, it will be about having programmable infrastructure with safe autonomy — infrastructure that understands your applications & manages itself without breaking production. 

### Conclusion

The benefits of cloud computing extend well beyond cost savings. Cloud computing has become the execution layer for modern business, enabling speed, resilience, experimentation, & global reach.

Its true value lies not in individual services, but in how it transforms organizational capabilities, & increasingly, in how intelligently you can automate those capabilities.

The most important question today is not whether cloud computing is less expensive.

It is whether your organization can leverage cloud computing autonomously — balancing cost, performance, & reliability without drowning in operational toil.

Without continuous optimization, the flexibility benefits of cloud computing are offset by the cost of resources that grow unmanaged. [Book a demo](https://sedai.io/demo) to see how Sedai keeps those costs in check as your cloud footprint expands.

When infrastructure manages itself safely, teams shift from reactive firefighting to proactive innovation. They spend less time tuning resources & chasing alerts, & more time driving product speed, competitive advantage, & business results.

This is what self-driving cloud infrastructure makes possible. Not just cheaper cloud. Better cloud.

The question isn't whether you should move to the cloud. The question is: are you ready for infrastructure that optimizes itself?

[Explore how Sedai makes your cloud infrastructure truly self-driving.](https://sedai.io/platform)

## FAQs

### What are the 5 most common uses of cloud computing?

The five most common uses are:

1. Application hosting
2. Data storage & analytics
3. Backup & disaster recovery 
4. Collaboration tools 
5. Development/testing environments

### Is cloud computing more secure than on-premises infrastructure?

Often, yes, especially for teams without large security organizations. Cloud security is a shared responsibility, but providers invest heavily in baseline protections.

### How does cloud computing improve business agility?

It removes infrastructure bottlenecks, allowing teams to respond quickly to changes in demand, customer behavior, & market conditions.

### What are the hidden productivity benefits of cloud computing?

You can optimize your productivity with:

- Faster setup
- Fewer operational interruptions 
- Easier experimentation
- Reduced cognitive load on engineering teams

### When might cloud computing not provide expected benefits?

Highly stable workloads, legacy architectures, or environments with strict regulatory constraints may require careful design to realize the full benefits of the cloud.

---

# How Autonomous Systems Differ from Automated Systems, and Why SREs Should Care
Source: https://sedai.io/blog/how-autonomous-systems-differ-from-automated-systems-and-why-sres-should-care

Understand Autonomous vs Automated Systems. Why SREs, DevOps should care as monolith-to-microservices shift revolutionizes application management. Learn more!

While the shift from monolith to microservices changed the game in regard to deployments and team velocity, it simultaneously introduced the monotony of daily repetitive work and manual tasks. SREs and DevOps now need to entirely rethink how teams manage their applications on a day-to-day basis.

To streamline this work, teams typically look to [automated systems](https://www.sedai.io/pricing) and smart alerts to help them stay on top of critical activity in their cloud environments. While a highly automated approach can certainly help with application management, these types of systems are not independently intelligent.

“Automated” and “autonomous” sound interchangeable and it seems like only a few extra letters are the difference, but there’s one important differentiator that bridges the gap between them: intelligence. An automated system can eventually graduate into an autonomous system. However, [autonomous systems](https://www.sedai.io/blog/announcing-availability-of-autonomous-cloud-management-platform) are not built on advanced automated systems. Confused yet? Let us define the basics.

If your team is constantly rewriting rules to keep up with workload changes, there is a better way. [Book a demo](https://sedai.io/demo) to see what autonomous cloud management looks like in production.

### Ready to move beyond cloud automation?

Book a Sedai demo to see how autonomous operations can improve reliability, performance, and efficiency.

## Understanding the difference

- **Automated systems** are essentially automated actions that are based on thresholds and built into a system. A user owns the rules around a certain situation and if something falls outside specific parameters, the system follows the prescribed action as defined by the user. The user takes on the burden of continuous learning. These actions might be as simple as an alert to the user, or a preset action that the user selected (for example, IFTTT).

- **Autonomous systems** are rooted in awareness about situations and intelligence to take action to achieve and learn from an outcome. They are smart enough to identify change without user-defined parameters, and inherently know how to react to mitigate the change. Autonomous systems are capable of independently identifying and acting and are only driven by a user’s problem statement (for example, “Help me reduce my latency”).

In other words, autonomous systems represent a dynamic, progressive [scale of intelligent capabilities](https://www.sedai.io/roles/platform-engineering), whereas automated systems are static — a predefined trigger yields certain results. Automated systems are not context-aware and are specifically told what to look *for*. In contrast, autonomous systems have a built in learning model and feedback loop and are told what to look *at*. Make sense?

So why are these terms so easily intermixed, and why is there so much confusion between the two? Two important reasons:

1. When teams automate systems and begin seeing **significant value**, they tend to call it an “autonomous system” — but that’s wrong. Even the most valuable, super-fast automated system is not autonomous because it fundamentally represents a different approach. Automated systems are inherently built on user’s knowledge – which represents highly aware, informed, and [intelligent decision making](https://www.sedai.io/platform/kubernetes-cost-optimization). That’s great, but it’s still simply synthesized down to “*do X when you see Y.*”
2. Autonomy has its own scale, and its lower levels appear to share similar characteristics as automated systems. 

## The Autonomy Scale 

Autonomous systems are responsible for handling decision-making, and leverage multiple inputs to monitor, evaluate, and measure information before feeding it back into the system. That’s one of the primary reasons they’re much more challenging to develop than automated systems — intelligence is critical to their success.

Autonomous systems can be judged by their level of intelligence and the complexity of their targeted action when scenarios arise. Let us look at an [intelligence scale](https://www.sedai.io/capabilities/release-intelligence) in regards to vehicles to illustrate the concept:

1. **Earth-bound**: You drive a stick shift vehicle (and there’s nothing wrong with that).
2. **Smart (but assisted)**: While on the highway, you turn on cruise control to maintain your speed. Further along, your car detects that you’re within 15 feet of the vehicle in front of you and automatically adjusts your setting to reduce your speed to 58 mph to maintain the distance between vehicles. Once the other vehicle is out of the way, your car returns your speed to 65 mph.
3. **Intelligent**: After your car reduced your speed while on cruise control, it analyzes the speed limit and determines how much faster your car can safely speed up to in order to make up for lost time while at the reduced speed.
4. **Space-Smart**: You sit in the backseat of the car and safely arrive at your destination without touching the steering wheel, gas pedal, or brakes.
5. **Beyond:** Your car learns that on Mondays you tend to be tired in the morning, and adjusts your typical route that day to pass by your favorite coffee shop to pick up a cup.

To be clear, we haven’t yet achieved the highest levels of autonomy yet. But the point is that the ultimate goal is to create a system that can think independently like a human to take the best and safest action possible. Most importantly, these types of systems need to be designed to allow a user to choose how they want to use it based on their comfort level — after all, the user should always be in control. 

‍

## 3 Reasons to Build an Autonomous System

How do you decide if an automated or autonomous system is right? As mentioned earlier, automated systems are capable of helping manage your applications. But being capable and being helpful are two very different things. Your team should consider the following when assessing what type of system your organization needs:

1. **Increased innovation & release velocity:** Developers used to release code monthly or biweekly but the new norm is to release new code in [increments of sub-seconds](https://www.netsolutions.com/insights/why-do-great-product-companies-release-software-to-production-multiple-times-a-day/), around the clock — which supports innovation and improves speed to market. That means your environments are constantly in a fluid state, and the chances of breaking something increase. Automation alone isn’t capable of effectively managing this ever-changing production environment. 
2. **Proliferation of microservices:** In past decades, it was common to have from a handful to hundreds of services powering operations. In [today’s cloud ](https://www.businesswire.com/news/home/20200716005101/en/O%E2%80%99Reilly%E2%80%99s-Microservices-Adoption-in-2020-Report-Finds-that-92-of-Organizations-are-Experiencing-Success-with-Microservices)though, that’s grown to hundreds of thousands of services working together to create a technology infrastructure. But [observing, managing and taking action](https://newrelic.com/blog/best-practices/data-culture-survey-results-faster-deployment) on all of these services is difficult. Dashboards that once gave visibility into hundreds of services now share “insights” on thousands of processes. Expecting an SRE to effectively manage this volume of services is an untenable solution. 
3. **Scale impacts everyone these days:** Five years ago scale was a challenge that only large companies like Google, PayPal, and Facebook faced. However, digital transformation has rewritten the narrative so that scale now impacts any organization in any industry, regardless of your customer count. Scale brings a litany of new problems, but most importantly it highlights the critical challenge that multiplying isn’t realistic. If your plan to scale involves a 1:1 ratio of assigned work to employees, you’re on a runaway train.

Automation brittleness during incidents is a reliability risk that autonomous systems are specifically designed to eliminate. [Book a demo](https://sedai.io/demo) to see how Sedai adapts to changing conditions and keeps your environment stable without manual intervention.

At the end of the day, “autonomous” versus “automated” isn’t simply a debate between which approach works best for your team and organization, nor is it about strategy in how to beat your competition. It's about having a system that can still operate efficiently without burdening teams-similar to the safety, agility, reliability and efficiency Tesla offers its drivers.  In today’s world of microservices, automated solutions for application management are increasingly a more challenging approach and will continue to be tested given the growing complexity of microservices. However, adding intelligence to these systems and evolving them into a truly autonomous system can help drive innovative transformation for the future.

### Frequently Asked Questions

Automated systems execute fixed, predefined rules, while autonomous systems perceive their environment, make decisions, and act independently. Automation repeats tasks; autonomy adapts to changing conditions.

#### How do autonomous systems differ from automated systems?

Modern environments change faster than humans or static scripts can keep up with, leading to alert fatigue and stale rules. Autonomous systems handle continuous optimization and remediation so SREs focus on higher-value engineering.

#### Why should SREs care about autonomous systems?

Not exactly. AI can power both, but automation still follows rules a human defined, whereas an autonomous system decides what action to take based on live signals and goals.

#### Are autonomous systems the same as AI automation?

Sedai autonomously analyzes application behavior and acts in production within guardrails to optimize cost, performance, and reliability, reducing manual toil for SRE and DevOps teams.

#### How does Sedai apply autonomy for SRE teams?

---

# Introducing Guardrails as Code: IaC Governance for Autonomous Optimization
Source: https://sedai.io/blog/guardrails-as-code

Autonomous optimization and IaC don't have to conflict. Introducing Guardrails as Code. Define optimization boundaries in Git, reviewed and versioned like any config change.

For engineering teams running a mature IaC practice, introducing autonomous optimization creates an inherent tension: any system that continuously adjusts infrastructure outside of Git creates drift — a gap between what's in your code and what's actually running.

Sedai's answer to that challenge is a set of three concrete integrations, and today we're announcing the latest: **Guardrails as Code**, now available for Kubernetes workload optimization. 

---

## **Sedai’s IaC Story So Far**

Sedai already offers two mechanisms for keeping your IaC in sync with our autonomous optimizations:

- **Loopback Updates** open a [pull request](https://docs.sedai.io/get-started/onboarding/readme/ci-cd#git-pull-request) in your Git repo within 24 hours of every validated optimization, so your IaC reflects what's actually running. 
- **CI/CD Overwrite Protection** is achieved via [Sedai Sync](https://docs.sedai.io/get-started/onboarding/readme/ci-cd#sedai-sync) for release-based pipelines, or the Live Sync Controller for GitOps environments like Argo CD and Flux. This feature prevents your deployment pipeline from reverting Sedai's changes.

Together, these handle the *after*: what happens once Sedai has acted. But there's a *before* side too, and that's what we’ve addressed with **Guardrails as Code**.

If your team is constantly rewriting rules to keep up with workload changes, there is a better way. [Book a demo](https://sedai.io/demo) to see what autonomous cloud management looks like in production.

---

## **Our Solution: Codifying Ranges, Not Fixed Values**

Guardrails as Code is the best way to optimize cloud infrastructure at scale, while still maintaining a source of truth.

Today, resource configuration values — CPU limits, memory requests, replica counts — are almost universally expressed as fixed numbers in IaC. A single value, committed to code, treated as a stable fact about a service. This made sense when infrastructure was static and changes were expensive. It makes less sense in a world where workloads are dynamic, cloud resources are elastic, and optimization systems can act autonomously and continuously.

A better model is already embodied in Kubernetes pod autoscaler configuration: HPAs and VPAs don't require a fixed replica count or a fixed memory value. Instead, we specify ranges — minimums and maximums — and offload the details to an intelligent system (Kubernetes) to operate within those bounds in real time.

This practice is standard, trusted, and pragmatic within the dynamic context of Kubernetes. As autonomous optimization becomes the new normal in the world of infrastructure management, we must embrace the same solution for the values that we are entrusting to autonomous systems, like CPU and memory allocation. As autonomous systems adjust configurations on the fly to reduce costs and protect performance, static values quickly become stale. As in the case of pod autoscalers, IaC in the context of autonomous optimization should define *the envelope of acceptable behavior*. An optimization system like Sedai should find the best value within that envelope, continuously, based on real signals. Guardrails as Code is how we are attempting to live up to this new reality.

---

## **Introducing Guardrails as Code**

Guardrails as Code lets you define the boundaries within which Sedai is allowed to operate — directly in your Git repository, as versioned YAML files, reviewed and merged like any other configuration change.

Think of it as a policy layer that lives in code. Before Sedai makes any optimization decision, it checks the applicable guardrails and constrains its option space accordingly. You might specify that a particular service should never run with fewer than 8 vCPU or more than 32 vCPU. You might set memory limits for a latency-sensitive service that can't tolerate aggressive downsizing. You might define account-wide defaults that apply unless overridden at the group or resource level.

A guardrails file looks like this:

If a guardrails file doesn't exist yet for a resource, Sedai will automatically create one via pull request with a proposed set of initial values (so you're not starting from a blank page) and the file still goes through your normal review process before taking effect.

Guardrails can be configured at three levels of granularity:

- **Resource level** — for services with specific requirements Sedai can't infer from signals alone (specialized hardware, contractual SLAs, etc.)
- **Group level** — apply consistent policies across a cluster, namespace, or tag-based group of resources
- **Account level** — set defaults that apply across everything, and override where needed with resource-level and group-level guardrails

**A note on scope:** Today, Guardrails as Code is available for Kubernetes workload optimization. Support for optimization of VMs and Kubernetes nodes is coming soon.

---

### Ready to govern autonomous optimization with confidence?

Book a Sedai demo to enforce policy-driven guardrails, automate cloud optimization, and maintain operational control.

## **How the Three Mechanisms Work Together**

Each mechanism addresses a distinct moment in the lifecycle of an autonomous optimization event:

1. **Guardrails as Code.** Before Sedai acts, it evaluates optimization opportunities against the ranges your team has defined in Git, bounding the decision space from the start.
2. **Loopback Updates.** Once a change is validated, Sedai opens a PR in your repo to bring your IaC in sync.
3. **CI/CD Overwrite Protection.** If your pipeline fires in the window between Sedai's change and that PR being merged, Sedai catches and corrects it.

Throughout all of this, Sedai is doing what it's designed to do: continuously analyzing your workloads, executing changes safely, and learning from the results.

With all three integrations in place, the full lifecycle looks like this:

---

## **A Deeper Look: Using Guardrails as Code**

### **Setting Up the Integration**

Guardrails as Code requires a Git integration to be configured in Sedai (Settings → Integrations), with the **Manage guardrails using this integration** option enabled. Sedai supports GitHub, GitLab, and Bitbucket Cloud.

Once the integration is active, you can enable guardrails management for your resources. This can be done at the resource level, group level, or account level from the Sedai UI. The actual guardrail values themselves live in Git from that point forward.

*Managing guardrails at the resource level*

### **Scope and Precedence**

When multiple levels of guardrails apply to the same resource, Sedai resolves conflicts using a precedence hierarchy: resource-level settings override group-level settings, which in turn override account-level defaults. This gives you the flexibility to set sensible defaults broadly while carving out exceptions for individual services or environments that need tighter or looser constraints.

### **The Guardrails File**

The guardrails YAML format is intentionally simple and human-readable. It specifies the resource name, region, and min/max bounds for the configuration dimensions Sedai can optimize — currently vCPU and memory for Kubernetes workload optimization. 

When Sedai proposes initial guardrail values, it bases them on observed utilization patterns — so the proposed ranges are grounded in real data, not arbitrary defaults.

Autonomous systems that operate within code-defined guardrails earn engineering team trust by making their boundaries explicit and auditable — not opaque. [Book a demo](https://sedai.io/demo) to see how Sedai implements guardrails-as-code in your environment.

---

## **Get Started**

**Guardrails as Code is available now for Kubernetes workload optimization.** If you're already a Sedai customer, you can enable it from Settings → Integrations. If you're new to Sedai, we'll explain how the full IaC integration story applies to your environment when you [book a demo](http://sedai.io/demo).

Support for Kubernetes and VM node optimization guardrails is coming soon. Stay tuned.

---

# 7 Strategies to Optimize Amazon ECS Costs
Source: https://sedai.io/blog/7-strategies-to-optimize-ecs-costs

These strategies aim to help AWS customers optimize Amazon ECS costs while being mindful of the complexities of ECS. By implementing these strategies, organizations can achieve improved performance, decreased costs, and heightened availability for their container-based applications.

### **‍**Introduction

In this article, we will discuss seven effective strategies to optimize Amazon ECS costs. This discussion is based on the joint webinar between Sedai and AWS, which you can watch [here](https://www.sedai.io/video/top-7-strategies-to-optimize-cost-for-aws-ecs). We will be covering different approaches to managing ECS clusters. In today’s market, the majority of cloud adopters are using container-based infrastructure, such as ECS. While ECS brings together favorable ease of use to containers, it is not without its complexities in managing cost, performance, and availability. Sedai and [AWS ](https://www.sedai.io/platform/aws-lambda-performance-tuning)have come together today to explore different options available to you in managing these complexities. 

Sedai has partnered together with AWS from a technical standpoint to deliver tremendous value to AWS customers, utilizing ECS, EKS, or Lambda to autonomously manage their resource allocation through an application lens for improved performance, decreased cost, and heightened availability, all in one platform. Our joint customers together are seeing as high as 50% reductions in cloud cost, 70% increases in performance, and as high as 6X productivity gains from their SRE and [DevOps teams.](https://www.sedai.io/roles/devops)

### Amazon ECS: A Quick Primer to Get Started

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[Amazon Elastic Container Service (ECS)](https://www.sedai.io/platform/aws-ecs-cost-optimization) 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 ](https://www.sedai.io/resources/aws-ec2-datasheet)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.

Lastly, [ECS services](https://app.sedai.io/signup) 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 encounter failure, the ECS service scheduler promptly launches a replacement task, guaranteeing the desired number of tasks is maintained for the service at all times.

### Importance of Optimization

Talking about the importance of optimization,  engineers and engineering leaders have different goals. Sometimes, they  have multiple goals. Sometimes, they have one direction that they go in. Sometimes, it's improving the customer's experience. How can you make things faster? Sometimes it's reducing costs. Sometimes, it's a balance of both. In this article, we'll go on talking about cost optimization, but we will try to keep a balance on how we can keep performance intact as well.

This is a [case study Sedai](https://www.sedai.io/resources) did across companies that are using ECS. This specific company is a security SaaS company, which is public.

‍

For every $100 million in revenue, approximately 8.6% was the cloud spent and it’s a very huge amount.

And if we could save 20% to 30% of that 8.6%, which means, on an average, if you can bring down the cloud costs to 6% of your revenue, we saw that the company's bottom-line revenue, bottom-line profit, grew a staggering 17%.

Just by optimizing your cloud cost to 20%, you could see your profitability going up by 70%. That's where cloud cost and performance optimization are critical.

Talking about the over-provisioning, and under-provisioning problem to start with.

‍

Datadog conducted a study among all their customers who use containers, managing billions of them. What they discovered was that nearly 50% of the containers utilize less than 30% of CPU and memory, indicating a waste of 70%. Why does this occur? Typically, engineering teams prioritize safety and availability, resulting in the inclusion of a buffer. They prioritize solving for availability first, which leads them to allocate extra CPU and memory. Occasionally, they also create additional replicas, contributing to this significant inefficiency. Another reason was, that companies sometimes have hundreds, thousands of services. It's very hard to right-size every service. There are buckets of computing profiles. That is a compute profile of 4 CPU 8 GB that's used across 100 services. There's another compute profile of 2 CPU 32 GB memory that's used across another 50 services. These two were the two common reasons, being safe, and on the other side, having common buckets of compute profiles created this over-provisioning, under-provisioning problem.

Anuj Sharma, Container Solution Architect at AWS, and Benji Thomas, President and COO at Sedai went through **7 Strategies to Effectively Optimize ECS** specifically, even though some of these practices can be applied across container environments.

‍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.

### Strategy 1: Service Right Sizing

The first strategy is to eliminate over-provisioning and under-provisioning. To save costs, you might have to adjust your provisioning. You don't want to under-provision because that will hit your availability. The first strategy is to safely right-size your service by eliminating over-provisioning and under-provisioning.

To do that exercise, as an engineer, what is the set goal?

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(Refer to the image above) On one side is availability. Meet your application demands.

You have typical traffic, max traffic, seasonalities, and unexpected spikes. As a performance engineer, or a software engineer, or an SRE, manage your application demands, not just today but also for the future.

On the other hand, wearing the dollar hat, you might want to save costs. You don't want to waste a lot of resources. That's the balance that you need to manage when you right-size a service, which includes a task as well.

When you do a thorough analysis through your load test and your profiles, you could right-size your service  at the same time, you can also discover hidden opportunities where you increase performance as well.

Some of the challenges in right-sizing your services, especially when you're dealing with many services, is that each service is released in today's microservice as well as services are being released very often. Each service has its own vertical and horizontal profile. 

Vertical profile is the memory CPU at the task level and the container level if you have a multi-container task. The horizontal profile is the number of tasks that you're running. That's one of the challenges that you need to solve.

Number two, you have your goal. Do you want to optimize for cost? Do you want to keep the cost the same? Do I improve performance? Also, I need to make sure all my seasonality is managed.

The approach to solve this is to right-size your service based on your seasonal needs and production behavior.

At the end of the day, if you do that for each of your services, you can run your workload in the most efficient way.

(Refer to the image above.) An example that you see is a memory-intensive application.

Due to the bucketing of standard compute profiles, initially, the service was at 4 vCPU 8 GB memory and needed 12 tasks, which the monthly cost was around $1,800 per month.

Once you've profiled it right, you understand that it's a memory-intensive application. You are able to bump up memory. You didn't need a lot of CPU, and you didn't need a lot of replicas. That way, you will be able to reduce your cost by more than 30% in this case. Sometimes you will get hidden opportunities where you will save performance as well.

### Strategy 2: Container Instance Optimization

**‍**After optimizing your service and task we move forward to the second strategy. This strategy is specifically focused on how you can optimize your EC2-backed cluster.

In the example that we discussed before, if we were to run 10 ports, and the application was a memory-intensive application, in serving a typical traffic that you have, and if you were to run 10 ports, and to serve that traffic, you were expected to run 10 ports, but in actuality, since it was a memory-intensive application, if you had a higher memory profile, you truly didn't have to run those many tasks or ports. What you need is a lesser number of tasks but with a higher memory profile.

Once you do that, you don't need to run the same number of container instances or the same number of the type of container instance. You could choose a container instance with a higher memory profile, and you could run a lesser number of container instances. That way, you save cost.

At the same time, by making that choice to pick a different profile, you have optimized your performance as well. Again, it's a win-win situation.

On the other hand, sometimes you could choose if you want to keep your costs or you want to increase performance. In that case, you might give it more power, adding more CPU, or adding more memory.

For that strategy, the key challenge is to first go back to strategy one. Optimize your service. Identify the right profile of your container instance, and then use that. Resize and rebalance your cluster that way you save on cost and performance.

### Strategy 3: Service and Cluster Auto Scaling

We already discussed optimizing your service, optimizing the cluster, and the container instances that it runs on. Are we ready for the seasonal spikes? Are we ready for the complex traffic patterns that are coming through? That's where service and cluster autoscalers are important. We have seen this practice where companies provision for the peak traffic that they get. For example, September comes in, and you're already ready for Thanksgiving. You've put in the maximum capacity that you need, which is a cost burden for you. That's not the ideal solution. You would want to keep your scale at the desired state, and you want to be ready for your seasonality patterns, your weekend-- a retail company may have a Monday-afternoon spike.

A tax firm may have a different type of spike. To manage a seasonality, we want to put the right autoscalers in place. You want to put your service auto scalers and your cluster auto scalers. There are a few things that we need to watch for when you put autoscalers. You don't want your autoscalers to behave badly. You need to make sure that you warm up your service continuously but not extensively. Also, when you're scaling, when you reduce your number of tasks, you want to do that concept conservatively and safely.

One example I've seen is, an application that may take a startup CPU spike. If you don't put the right warm-up time frame. For example, have a 10-second warm-up time frame and your application takes more than 10 seconds to warm up, you're going to get the cycle of autoscaling happening. You always want to put the right warm-up values and right cool-down values in your scale-out and scale-in profiles. 

‍

We talked about right-sizing your services, right-sizing your clusters, and putting the right autoscalers in place. Comparing our before-and-after values, if you always plan for your peak traffic, you're always putting in the maximum that you need, you end up spending more. But if you put the right autoscalers in place, you have a desired count, which covers most of your scenarios, and then you have a maximum, and then you have scaling in place to get to your max and scale down to your minimum when it's not required.

### Strategy 4: Enable Telemetry

Enabling telemetry doesn't sound like a strategy, it sounds like a best practice. We wanted to call it out as a strategy because we have seen occasions where companies are not doing this extensively.

Many times, companies only watch CPU and memory, and then they make decisions based on that. What happens is that performance is affected. Sometimes, the other specs go up.

We wanted to call this strategy out to observe, observe, observe your applications, your metrics, your events, your logs, and your telemetry. When you make these optimization choices, make sure you've verified all these for not just your saturation, but you've also verified your performance metrics, which are your traffic and latency events, and also how your dependencies behave using tracers.

Amazon AWS announced Service Connect last year. A lot of this comes in a packed, feature-rich by default with Service Connect. Your discovery, how you connect between services, and even performance metrics are available by default in Service Connect.

When you apply that strategy, the before-and-after picture is typically like this. So you have a before-and-after, which we talked about in the previous slides.

But if you're not observing, you don't know your performance. You're making decisions in the vacuum. But if you know your performance metrics, if you know your dependencies, if you know your events, then you compare everything, and you're confident that the decisions that you have made are good and it works for your bottom line.

### All Seven ECS Cost Optimization Strategies Executed Continuously From a Single Autonomous Platform

See how Sedai autonomously implements all seven ECS cost strategies including Fargate rightsizing, Spot management, and idle resource reclamation from one platform.

### Strategy 5: Purchasing (Savings Plan, Spot)

Before we dive deep into the strategy, let's talk about the four different purchasing options available for EC2. 

‍

Spot instances are placed as one of the four purchasing options available to customers when you are using EC2, which you are probably familiar with at this point of time. 

The standard on-demand pay-as-you-go by the second is best for fluctuating workloads from a cost-optimization perspective. 

Reserved instances and savings plan, where you commit your usage in a more-or-less flexible way in exchange for significant discounts. It's perfect for steady usage, per se.

And finally, spot instances, which constitute spare capacity in EC2 or Fargate, are available at very steep discounts for up to around 90% of on-demand prices. These are best suited for stateless or fault-tolerant workloads, and we will see why as we go on.

‍

As you all know, when you run your easiest task, either in an EC2 instance, where you as a customer have the full control over the operating system where your easiest task will be scheduled. Or you can choose to run your ECS task in Fargate mode, where AWS relieves you from the operational burden of managing and patching the underlying operating system and managing them for you.

In both of these options, you have the option to apply a spot-pricing model.

You get the obvious benefits of price in exchange for a two-minutes warning for reclaiming but EC2 spot specifically lets you choose the instance pool whereas in Fargate spot you get a diversification automatically.

‍

Any of these purchase options that we just went through rely on the exact same underlying EC2 infrastructure, available across AWS region. There is no difference in the behavior of these instances. Spot is no exception. Spot instances are just idle capacity that is not being used to fulfill the on-demand request, and they are made at cheap prices.

Very important to note here, although the price of spot instances changes in time, it is in no way determined by the highest bid anymore. Bidding is altogether a thing or a concept in the past for spot. Instead, the price calculation now is based on the long-term demand and supply trends for the instances.

Finally, there is only one difference between on-demand and spot instances. The word "spare" implies something, of course, which is that when EC2 instances need the capacity back, spot instances can be interrupted, with the instance receiving a notice that termination will happen two minutes after the fact. This is why spot instances with fault-tolerant or stateless workload are better, where an interrupted instance is replaceable with spot capacity when available with little or no impact to your workload.

‍

Speaking of capacity, it is divided into what we call spot instance pools. Each of these audience inquiries that you see represent a spot instance pool.

Each one is a combination of specific instance type, size, availability, zone, and region. This really adds up to a lot of pools.

The price and capacity of these pools fluctuates independently from each other. If you use the same instance type on three different availability zones, you are effectively consuming capacity from three different spot pools.

Tying this back to the spot interruption, it is easy to see that the more instance pools you use, the more diverse you are, which means that you decrease the chances of a large portion of your sport instance being interrupted if demand increases. It's just like how the saying “Don't put all your eggs in one basket” goes.

‍

At this stage in the lifetime of spot, with bidding completely gone, and all the new capabilities we have, the data shows us that the interruptions have become fairly infrequent, with only 5% of spot instances that have been interrupted in the last three months or so. The more customers adopt the best practices and properly diversify, the better spot as a whole will function.

‍

As a good strategy, it's best to add a mix of on-demand instances and spot instances. Even if you end up over-provisioning, let's say, by 50%, just by introducing one third of spot and remaining on-demand, you are likely getting around 5% to 10% of saving in cost.

‍

Now, let's cover how to implement spot interrupt handling in ECS as a service. Let's see how that works in this area. The good news is, this can be automated just by setting one parameter, which is ECS Enable Spot Instance Draining to True for the ECS Container Agent on EC2 instance via user data during the instance creation time. This effectively drains the instances from ECS control plane and stops all the pending tasks that are scheduled on that instance.

ECS does this by setting all the tasks running on that instances to draining by sending a SIGTERM signal, let's say, at T0, and a SIGKILL signal 30 seconds after. This lets you stop your application gracefully, or even do the last mile log collection.

ECS will also deregister all the tasks from the load balancer target group. And then ECS will try to reschedule the task on the remaining instances that you have available.

Therefore, if I have to summarize this strategy, the implementation should consider architectural choices such as dynamically including a mix of on-demand and spot instances, optimal multi-year commitment designed to recover from interruptions. And most importantly, consider the profile of your workloads, and see what kind of purchasing option can it fit in. 

‍

As a sample calculation, just by introducing a mix of on-demand, spot, reserved instance, or using savings plans, you are looking forward to roughly around 50% reduction in the overall cost.

### Strategy 6: Tagging

‍

Tagging EC2 instances and your task enables you to get a full visibility on the cost. Each tag is a simple label consisting of a key and an optional value to store information about the resource or the data written in that resource. Tagging addresses a variety of use cases, whether it be cost and finance management, operations or support, or even data management or access controls, per se.

For today's discussion, however, we will on focus tagging for the cost and finance management use cases only. Cost allocation refers to the assignment or distribution of the incurred cost to the users or the beneficiaries of those costs following a defined process. We divide the cost allocation into two types, showbacks and chargebacks. Showback tools and mechanisms help increase the cost awareness, whereas chargeback helps with the cost recovery and drives enablement of cost development.

Showback is about presentation, calculation, and reporting the charges incurred by a specific entity, let it be an EC2 instance or a specific task on ECS, such as business unit application, user, or cost center. It is important to devise a tagging strategy that addresses all your organizational needs, not only limit it to cost and finance management.

Overall, your tags could be a combination of region, and environment, let it be dev environment, test environment, so forth. Application tiers such as middle-tier, front-end, database-tier, or a specific unique identifier for that application, depending on how your organization is structured, and the overall objectives that you want to fulfill with tagging.

Tag enforcement could be either reactive or proactive, depending on the migration and the modernization journey that you are currently going through, and depending on which phase you are in this journey. Consider using tools such as tag policies, or service control policies, or IAM policies, et cetera, to enforce proactive tagging.

And implementing these typically have a tendency to break your existing automation for the workloads. Whereas reactive tagging, such as tag editors, or AWS config rules, or custom Lambda functions, typically do not break your existing automation. 

**Proactive:**

- Good for new workloads and when considering tags in the project design phase
- Could break automation for existing workload
- Can work only if the API’s support atomic operations (similar to SCP or IAM)
- AWS Tools: Tag Policies, AWS Organization SCP, IAM Policies, Service Catalog tags

**Reactive**

- Good for existing workloads
- Possibility to have compliance reports to provide a list of non compliant resources to action, manually or automatically
- Does not break existing automations
- AWS Tools: Tag Editor, AWS Config rules, Custom lambdas

A combination of both of these worlds in terms of proactive and reactive tagging is considered best for effective tag management.

Fargate task rightsizing, Spot capacity management, and idle resource cleanup are each a full-time job. [Book a demo](https://sedai.io/demo) to see how Sedai executes all seven ECS cost strategies continuously from a single platform.

### Strategy 7: Scheduled Shutdown

‍

The benefit of cloud comes specifically from the costing standpoint. It is best realized when you use the resources when you need it, and if you don't need it, don't use the resources. This effectively ensures that you are optimizing your OpEx versus comparing it to a traditional model of high CapEx, per se.

You may have a dev environment that you don't use over the weekends, or in the night time, per se. Consider shutting them down if you don't need it. AWS provides solutions such as AWS instance scheduler, which automates the starting and stopping of EC2 instances.

As an example, if you just consider shutting down your environment or your dev environment, per se, which you may not use it over the night or over the weekend, it will result you approximately a cost saving of around 29% If you operate just 24-cross-five, just five days in a week, when you compare it against running 24-cross-seven. So that by itself is giving you around 29% benefit in cost.

### Autonomous Optimization

We have talked about all these strategies- how you could optimize for cost and performance, optimizing your services, clusters, doing your telemetry right, targeting well, using purchasing options wisely and using schedulers.

The challenge now is, how best can you implement it? You could do it all manually. You could write your automation. And there are multiple levels of automation. You could write automation that is, again, manually triggered. You could write automations that are automatically triggered but without any feedback loop. Or you could be autonomous where you have automation. There's a feedback loop coming in. You learn from it. You understand the efficacy. And then you improve on your actions in the next phase.

‍

To give a recap, we went through multiple strategies and different controls and levers that you could adjust to optimize your services. These are things that are in your control.

As engineers managing the cloud, there are things that are not in your control which are the traffic, seasonality patterns, and the releases that happen. There are inputs and there are knobs that we could manage and this defines the complexity of how you could optimize, and how you could apply all those learnings that we talked about.

So let me take an example of a company with 100 microservices.

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Average release is one time per week. Each of these microservices, at least once per week, and there are different performance metrics to be analyzed each time it is released. And there are different seasonality patterns for the service. Even within a company with a specific seasonality, there'll be a group of microservices that follows a different seasonality. A retail company doesn't mean all the microservices follow the standard online purchase model. There are applications with different seasonalities. It's important to analyze the traffic seasonality and how they behave when we optimize them

Look at the staggering number of combinations of knobs that you could adjust in the scenario.

That's where a higher level of automation or autonomous actions are more important. You don't want to just write random automation and leave it in there. You want to put all these things in practice, but you want to make sure that there are knobs in place to learn and improve on that.

The ideal scenario is you have high-level goals.

‍

What do you want to achieve for a group of applications? You have tagged your applications well. For a group of applications, do you want to hit a cost-duration balance?

Do you want to improve performance? Do you want to decrease cost? That should be the goals that we operate on.

On the other hand, if you're managing with templates per service, you end up with hundreds of templates that need to be modified every release. For individual services and tens of services, plus your auto scalers and your service autoscalers.

Ideally, you don't want to shift left your production runtime responsibilities. You want to shift it up where you have an autonomous system in place, where it optimizes your runtime parameters. You still need your templates for your software versions, your dependencies, and all those things. But shift up is the right strategy to manage your runtime parameters.

‍

The actual validation of your actions and how it behaves can be missed. You can enhance the automation to do that. But automation becomes autonomous when you do all five of these.

‍

If you were to do this by yourself, typically, you have a fleet. You have multiple services. They're all being observed well. That means the telemetry is perfect. Ideally, you want to build an autonomous platform where your topology is being discovered frequently. You're watching your metrics 24/7. Every time you're identifying your app behavior based on the releases, you safely reconfigure them, and optimize them.

You identify what happened and validate the efficacy of the action. You learn from it and then keep repeating the loop. That's how a typical autonomous system works.

### Frequently Asked Questions

Key strategies include right-sizing task CPU and memory, using Spot capacity, applying Savings Plans, scaling on real demand, scheduling shutdowns for non-production, and removing idle services. Combining them maximizes savings.

#### What are the best strategies to optimize ECS costs?

Overprovisioned task definitions and idle non-production environments are the most common waste, since teams size for peak and rarely revisit it. Continuous right-sizing recovers this spend.

#### What is the biggest source of ECS cost waste?

Shutting down or scaling down development and staging environments outside working hours eliminates payment for idle capacity, often cutting non-production cost substantially.

#### How does scheduling reduce ECS costs?

Sedai autonomously rightsizes ECS tasks and aligns capacity with demand continuously, capturing savings across strategies without manual tuning or risk to availability.

#### How does Sedai optimize ECS costs at scale?