Spot instances in Kubernetes are discounted compute resources offered by cloud providers like AWS, utilizing their excess capacity. They are significantly cheaper than on-demand instances—often providing savings of 70-90%—but can be reclaimed by the provider with minimal notice. This makes them ideal for scaling workloads affordably, especially for non-critical or flexible applications. Source
AWS can reclaim spot instances when demand for on-demand capacity spikes, providing a termination notice typically about two minutes in advance. This short warning period requires applications to be architected for resilience and rapid failover. Source
To set up Kubernetes on spot instances, you need AWS IAM roles and permissions, as well as Kubernetes tools like kubectl and eksctl. Access to EC2 spot instances is essential for configuring node groups and autoscaling. Source
Node groups for spot instances are configured using YAML files, where you define instance types, scaling policies, and apply taints and tolerations to control pod scheduling. This ensures spot instances handle lower-priority workloads and can be replaced seamlessly if interrupted. Source
The Cluster Autoscaler automatically adds or removes nodes based on workload needs. When configured to prioritize spot nodes, it helps maximize cost savings by scaling up spot instances during demand spikes and scaling down when demand subsides. Source
Taints and tolerations allow you to control which pods are scheduled on spot nodes. By marking spot nodes with taints and assigning tolerations to non-critical workloads, you ensure that only appropriate workloads are placed on spot instances, protecting critical applications from interruptions. Source
Best practices include diversifying instance types, using multiple availability zones, balancing spot and on-demand instances, and configuring node groups with proper labels, taints, and tolerations. These strategies improve resilience and cost efficiency. Source
Spot instance interruptions are managed by tools like the AWS Node Termination Handler, which listens for interruption notices and triggers pod rescheduling. Sedai’s platform uses predictive analytics to anticipate interruptions and optimize resource distribution, reducing manual intervention. Source
To mitigate risk, maintain a baseline of on-demand instances for critical workloads, automate pod rescheduling, use persistent storage, and diversify instance types and availability zones. These strategies ensure high availability and minimize disruption. Source
Sedai’s platform uses real-time monitoring and AI-driven optimization to predict interruptions, adjust configurations, and maintain stability across clusters using spot instances. It automates node group configuration and scaling, minimizing manual intervention and maximizing cost savings. Source
Sedai’s platform manages pod scheduling by adjusting affinity and toleration settings, ensuring an even distribution of workloads. This minimizes disruption during interruptions and helps maintain application stability. Source
AWS spot instances offer up to 90% discounts with a 2-minute preemption warning. GCP Spot VMs provide deep discounts (sometimes 90%) with stable pricing and a 30-second warning. Azure Spot VMs also offer deep discounts and a 30-second warning. Each provider has unique pricing and interruption policies, so multi-cloud strategies can enhance resilience. Source
Monitor spot instance usage with AWS metrics, CloudWatch alarms, and Kubernetes tools like Prometheus. These tools track instance lifecycle, spot price, interruption notices, and resource allocation, helping you respond quickly to changes. Source
Strategies include using fixed or dynamic bidding, diversifying instance types and pools, setting CPU/memory-based autoscaling policies, and leveraging cost optimization platforms like Sedai to automate bid adjustments and scaling. Source
Sedai continuously monitors, predicts, and optimizes configurations, balancing cost savings with reliability by minimizing manual interventions and optimizing for cost-efficiency. It dynamically adjusts node groups and instance selection to maintain stability. Source
Delivery Hero transitioned their entire Kubernetes infrastructure to spot instances, demonstrating large-scale viability. ITV used spot instances to handle increased viewership and optimize costs during the pandemic. Read more
You can use kubectl for manual scaling or rely on Sedai’s platform, which dynamically scales applications based on real-time demand. This ensures stability and cost efficiency without over-provisioning. Source
Monitor your spot instance usage through AWS’s console and CloudWatch metrics. If you anticipate high demand, request quota increases to scale your cluster as needed. Source
Sedai’s autonomous optimization platform predicts interruptions, optimizes configurations, and maintains stability, enabling you to scale efficiently while minimizing costs and manual intervention. Learn more
Sedai offers autonomous optimization, proactive issue resolution, full-stack cloud coverage (compute, storage, data across AWS, Azure, GCP, Kubernetes), smart SLOs, release intelligence, plug-and-play implementation, multiple modes of operation (Datapilot, Copilot, Autopilot), enhanced productivity, and safety-by-design. Source
Yes, Sedai integrates with Cloudwatch, Prometheus, Datadog, Azure Monitor, GitLab, GitHub, Bitbucket, Terraform, ServiceNow, Jira, Slack, Microsoft Teams, and various runbook automation platforms. Source
Sedai uses machine learning to optimize cloud resources for cost, performance, and availability without manual intervention. It continuously learns from interactions and outcomes to improve optimization and decision models over time. Source
Release Intelligence tracks changes in cost, latency, and errors for each deployment, improving release quality and minimizing risks during deployments. Source
Sedai offers three modes: Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution of optimizations). Source
Sedai integrates with Infrastructure as Code (IaC), IT Service Management (ITSM), and compliance workflows, ensuring all changes are safe, validated, and auditable. Source
Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. Source
Detailed technical documentation is available at docs.sedai.io/get-started. Additional resources, including case studies and datasheets, are at sedai.io/resources.
Sedai addresses cost inefficiencies, operational toil, performance and latency issues, lack of proactive issue resolution, complexity in multi-cloud environments, and misaligned priorities between engineering and FinOps teams. Source
Sedai is designed for platform engineers, IT/cloud ops, technology leaders, site reliability engineers (SREs), and FinOps professionals in organizations with significant cloud operations across industries such as cybersecurity, IT, financial services, healthcare, travel, and e-commerce. Source
Customers can achieve up to 50% cloud cost savings, 75% latency reduction, 6X productivity gains, and 50% reduction in failed customer interactions. For example, Palo Alto Networks saved $3.5 million, and KnowBe4 achieved 50% cost savings in production. Source
Customers highlight Sedai’s quick setup (5–15 minutes), agentless integration, personalized onboarding, comprehensive documentation, and risk-free 30-day trial as key factors for ease of use. Source
For general use cases, Sedai’s setup process takes just 5 minutes. For specific scenarios like AWS Lambda, it may take up to 15 minutes. More complex environments may vary. Source
Industries include cybersecurity (Palo Alto Networks), IT (HP), financial services (Experian, CapitalOne Bank), security awareness training (KnowBe4), travel (Expedia), healthcare (GSK), car rental (Avis), retail/e-commerce (Belcorp), SaaS (Freshworks), and digital commerce (Campspot). Source
KnowBe4 achieved 50% cost savings and saved $1.2 million on AWS. Palo Alto Networks saved $3.5 million and reduced Kubernetes costs by 46%. Belcorp reduced AWS Lambda latency by 77%. KnowBe4, Palo Alto Networks
Notable customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis. Source
Sedai offers 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack cloud coverage, release intelligence, and plug-and-play implementation. Many competitors rely on static rules, manual adjustments, or focus on specific areas, while Sedai provides a holistic, autonomous solution. Source
Unique features include 100% autonomous optimization, proactive issue resolution, application-aware intelligence, release intelligence, and a quick setup process (5–15 minutes). Source
Sedai uses AI-driven, real-time optimization and predictive analytics to manage spot instances, while traditional tools often rely on manual intervention or static rules. Sedai’s approach minimizes manual effort and maximizes cost savings and reliability. Source
Platform engineers benefit from reduced toil and IaC consistency; IT/cloud ops see lower ticket volumes and safer automation; technology leaders gain measurable ROI and reduced spend; FinOps teams align engineering and cost goals; SREs experience fewer alerts and less manual work. Source
Hari Chandrasekhar
Content Writer
March 10, 2025
Featured
Imagine managing a Kubernetes cluster with fluctuating demands and constantly rising costs. Every scaling decision brings added expenses, and traditional instance pricing often doesn’t allow you to scale affordably. Enter spot instances. Running Kubernetes clusters on spot instances isn’t just a cost-saving strategy—it’s an approach that makes scalability and flexibility possible at a fraction of the price.
By using AWS spot instances in Kubernetes, you’re tapping into Amazon’s surplus capacity, gaining significant savings and making it easier to meet demand surges without blowing through budgets. Spot instances in K8s are becoming increasingly popular, but they come with their own set of considerations, especially around reliability and potential interruptions. That’s where this guide comes in.
This article covers everything from setting up spot instances for Kubernetes clusters to leveraging autoscaling and best practices for managing spot instance interruptions. Additionally, we’ll explore how Sedai’s autonomous optimization platform (Learn more at Sedai) simplifies the process with real-time scaling and interruption management.
Source: Sedai
Spot instances offer an affordable way to scale Kubernetes clusters by utilizing AWS’s unused EC2 capacity at discounted rates. The key benefit here is cost-effectiveness—by deploying your Kubernetes workloads on spot instances, you can save as much as 70-90% compared to standard on-demand instances. But with those savings come potential challenges: spot instances can be reclaimed by AWS with as little as two minutes' notice.
With Sedai’s AI-powered tools (Discover Sedai’s solutions), this risk is minimized by continuously monitoring instances and adjusting configurations to ensure consistency. Let’s examine why spot instances have become a popular choice for Kubernetes deployments.
Spot Instance Bidding StrategiesAWS Spot Instances uses a bidding model where you specify the maximum price you're willing to pay for an instance. AWS will automatically choose the lowest-price capacity available as long as it’s under your bid. Here are some key strategies to effectively manage bidding:
To begin using spot instances in K8s, you’ll need AWS IAM roles and permissions, as well as essential Kubernetes tools like kubectl and eksctl. These tools will allow you to access and configure your EC2 spot instances.
Requirement | Description |
|---|---|
AWS IAM Roles | Required to access and manage EC2 instances, including spot instances for Kubernetes clusters. |
Kubernetes | Tools like kubectl and eksctl are essential for configuring and managing your cluster. |
Access to EC2 | Setting up access to EC2 spot instances is critical to configure node groups and autoscaling. |
Let’s walk through creating a Kubernetes cluster configured specifically for spot instances:
Example YAML Configuration for Node Groups:
Yaml
By following these steps, you can set up your Kubernetes cluster to maximize cost savings and scalability. Additionally, Sedai’s autonomous node group configuration allows for real-time adjustments, ensuring minimal disruption when spot instances are reclaimed (Explore Sedai’s node group configuration).
Spot Fleets lets you provision a mix of on-demand and spot instances to meet your needs for both cost and reliability. When configuring Spot Fleets for Kubernetes, consider:
Example:
yaml
Source: Sedai
Autoscaling is essential for managing workloads efficiently on spot instances, allowing your Kubernetes cluster to automatically scale up or down based on demand. When using spot instances, the Cluster Autoscaler becomes a vital tool in this setup, especially when configured to prioritize spot nodes. Let’s break down how you can leverage autoscaling and optimize it for cost-efficiency.
The Cluster Autoscaler is designed to add or remove nodes based on workload needs, making it a perfect partner for spot instances. When the demand increases, the autoscale can add more spot instances, scaling down when the demand subsides to avoid unnecessary costs. For effective use:
Autoscaling plays a pivotal role in ensuring that your Kubernetes workloads are cost-efficient while still maintaining performance and availability.
When using spot instances, it's crucial to adjust scaling based on real-time resource usage and demand fluctuations. Here are some detailed best practices to optimize autoscaling for spot instances:
To maximize the cost-saving benefits of spot instances, configure your autoscaling policies to dynamically adjust the number of instances based on resource consumption, such as CPU and memory usage. By doing so, your Kubernetes cluster can scale up during peak demand and scale down during idle times, all while leveraging the lower cost of spot instances.
Example: A policy that adjusts the number of spot instances based on CPU usage could be written as follows:
yaml
This policy automatically adjusts the number of pods in the example-deployment based on CPU utilization, ensuring that the resources are dynamically scaled based on demand.
2. Diversify Instance Pools
One of the primary risks of using spot instances is the possibility of simultaneous termination of all instances within a single instance pool due to demand spikes or availability issues.
To mitigate this risk, diversify instance pools by using multiple instance types and availability zones. This practice minimizes the chances of all your instances being interrupted at the same time, ensuring that your Kubernetes workload can remain operational.
Example: A Kubernetes configuration defining node pools with different instance types and multiple instance pools:
yaml
This example sets up a Kubernetes node group with three different instance types across three different instance pools, ensuring both cost efficiency and fault tolerance in case of spot instance terminations.
3. Use Taints and Tolerations for Optimized Workload Distribution
By using taints and tolerations, you can fine-tune your Kubernetes workloads to ensure that only appropriate workloads are scheduled on spot instances. This allows you to prioritize critical workloads on more stable instances, while running non-critical tasks on spot instances, further optimizing resource allocation.
Example: Configuring taints and tolerations for a spot instance node group:
yaml
This configuration ensures that only non-critical workloads with the corresponding tolerations are scheduled on the "spot-ng" node group, while critical applications are scheduled on other on-demand node groups.
4. Implement Instance Diversification and Availability Zones
As mentioned earlier, the use of multiple instance types and availability zones can dramatically increase the reliability of your Kubernetes cluster. When spot instances are reclaimed, the more diverse your instance types and availability zones, the higher the chances that your workload will remain operational.
Example: A diversified instance pool across multiple availability zones:
yaml
This setup spreads the spot instance node group across three availability zones and pools, reducing the risk of losing all instances at once.
To mitigate the risk of sudden spot instance terminations, consider the following strategies:
Source: Sedai
Spot instance interruptions are a reality; AWS can reclaim these instances with minimal notice. Preparing for this ensures your applications remain resilient. Let’s look at how to handle interruptions effectively.
AWS reclaims spot instances when demand for on-demand capacity spikes. When this happens, AWS provides a termination notice, typically about two minutes in advance. Handling these interruptions gracefully is crucial to avoiding service disruptions.
The AWS Node Termination Handler is a Kubernetes add-on that intercepts termination notifications and triggers rescheduling for affected pods. Here’s a simple setup guide:
With Sedai’s platform, interruption handling becomes even more proactive. Using predictive analytics, Sedai anticipates interruptions before they occur and optimizes resource distribution, reducing dependency on manual intervention (Explore Sedai’s interruption handling).
Spot Instance Monitoring and MetricsTo ensure smooth operation with spot instances, continuous monitoring is essential. Key AWS metrics for spot instances include:
Managing Spot Instance QuotasAWS enforces limits on the number of spot instances you can request in a given region. To manage these quotas effectively, you can:
Adopting best practices can significantly reduce the risk of disruptions when running spot instances in Kubernetes. Here are some tried-and-tested strategies.
Using multiple instance types across different availability zones improves resilience and decreases the risk of spot interruptions. When you diversify instance types, your cluster can adjust more flexibly during interruptions by drawing from a broader capacity pool.
Node groups play a critical role in managing workloads on spot instances. Configure node groups with proper labels, taints, and tolerations to ensure that workloads are efficiently handled and to control which applications are assigned to spot nodes.
While spot instances offer impressive cost savings, it’s essential to maintain reliability for critical workloads. Use a blend of spot and on-demand instances to ensure high availability. Sedai’s real-time monitoring helps maintain this balance by optimizing node groups and making informed instance selections, allowing you to maintain stability without excessive costs.
Strategy | Benefits |
|---|---|
Instance Type Diversification | Improves resilience and flexibility, minimizing impact from interruptions. |
Sedai’s Node Group Optimization | Dynamically adjusts node settings for performance and cost efficiency. |
Balancing Spot and On-Demand Instances | Ensures critical workloads remain stable while achieving cost savings. |
Running applications on spot instances requires specific configurations to handle scalability and potential interruptions effectively.
When deploying applications on spot nodes, it’s essential to configure for resilience. Use labels and taints to direct non-critical applications to spot nodes, ensuring that mission-critical applications are reserved for stable, on-demand instances.
Kubectl commands make managing deployments and scaling on spot instances efficient. Here are some basic commands for managing application deployments:
bash
Sedai’s platform enhances application scaling by dynamically adjusting scaling strategies based on real-time performance and usage. This means applications maintain stability, even during rapid scaling, without additional manual intervention.
Scaling Strategy | Description |
|---|---|
Kubectl Scaling Commands | Allows quick and easy scaling of applications to handle workload surges. |
Sedai’s Real-Time Scaling | Automatically adjusts scaling based on current demand, ensuring efficient resource use by continuously monitoring workload metrics like CPU, memory, and traffic. This dynamic adjustment helps maintain optimal performance without over-provisioning, keeping costs low and resource utilization high. |
Here is the comparison of top cloud providers’ spot instance offerings:
Amazon Elastic Compute Cloud (EC2)
Instance Type | CPU Type | CPU GHz / RAM / SSD | Price ($/Month) | Spot ($/Month) |
|---|---|---|---|---|
c6g.large | AWS Graviton2 | 2.5 / 4 / 30 | 52.04 | 32.68 |
c5a.large | AMD Rome | 3.3 / 4 / 30 | 58.61 | 32.75 |
c6i.large | Intel Ice Lake | 2.9 / 4 / 30 | 64.45 | 32.8 |
c6a.large | AMD Milan | 1.95 / 4 / 30 | 58.24 | 33.11 |
c7g.large | AWS Graviton3 | 2.6 / 4 / 30 | 55.18 | 33.01 |
Amazon EC2 Spot Instances advertise discounts of up to 90%. In reality, the discounts vary significantly per region, type, and even instance size and end up being much more modest if you require a specific instance type/size. They can also change quickly (their pricing tool is updated every 5 minutes), and they allow you to set a maximum price you are willing to pay (with your VM terminated if the market price exceeds that). In general, the discounts that are obtained for the tested instances are 50% or less.Even though the discounts are not as spectacular as other providers, Amazon offers a very generous 2-minute preemption warning, which should allow most tasks to gracefully end (and perhaps even time for a replacement instance to spin up). In addition, AWS makes it easy to set up their Auto Scaling with Spot instances, requiring less work to include Spot instances in your setup.
Google Compute Engine (GCE)
Instance Type | CPU Type | CPU GHz / RAM / SSD | Price ($/Month) | Spot ($/Month) |
|---|---|---|---|---|
n2d-c2-4096 | AMD Milan | 2.45 / 4 / 30 | 45.78 | 10.68 |
t2d-s2 | AMD Milan | 2.45 / 8 / 30 | 64.68 | 12.77 |
c2d-hcpu2 | AMD Milan | 3.05 / 4 / 30 | 57.72 | 14.6 |
t2a-s2 | Ampere Altra | 3.0 / 8 / 30 | 59.21 | 19.86 |
n2-c2-4096 | Intel Cascade Lake | 2.8 / 4 / 30 | 52.15 | 17.18 |
n2-c2-4096 | Intel Ice Lake | 2.6 / 4 / 30 | 52.15 | 17.18 |
e2-c2-4096 | Intel Broadwell | 2.2 / 4 / 30 | 43.38 | 18.12 |
n1-c2-4096 | Intel Skylake | 2.0 / 4 / 30 | 45.99 | 18.32 |
c2-s4 /2* | Intel Ice Lake | 2.7 / 8 / 30 | 63.99* | 9.92 |
c3-hcpu4 /2* | Intel Sapphire R | 2.7 / 4 / 30 | 65.91* | 8.71 |
GCP Spot VMs offer some of the most generous discounts for computing resources among providers. What's more, the prices are not very different from region to region and are stable enough (can only change up to once per month) for them to be on the main price list with no need for special pricing tools. Also, the discounts are only different per type, regardless of instance size. All this allows you to plan ahead and easily figure out what you are paying.
From the table, you can see some amazing savings, with the C3 being a big standout. That's the latest Sapphire Rapids type, so it would seem that Google's customers have not yet started using it en masse, and there is a lot of spare capacity. At full price, we saw that the C3 was not great value, but at a 90% discount, this should be a whole different discussion!
The GCP Spot VMs we are using seem to have a lifetime measured often in hours and sometimes in days, depending on the type and region. However, the preemption warning Google offers is 30 seconds, which is less generous than AWS.
Microsoft Azure
Instance Type | CPU Type | CPU GHz | RAM (GB) | SSD (GB) | Price $/Month | Spot $/Month |
|---|---|---|---|---|---|---|
D2pls_v5 | Ampere Altra | 3.0 | 4 | 32 | 52.04 | 7.36 |
F2s_v2 | Intel Cascade L | 2.6 | 4 | 32 | 64.16 | 14.87 |
D2ls_v5 | Intel Ice Lake | 2.8 | 4 | 32 | 64.45 | 27.22 |
D2as_v5 | AMD Milan | 2.45 | 8 | 32 | 65.18 | 8.68 |
The Azure Spot Virtual Machines behave like on AWS, with prices that can vary over time and preemption when the market price is above your set maximum. However, the actual discounts are much deeper than AWS for most types. A preemption warning is also similar to GCP at 30 seconds; therefore, the pattern is quite similar to GCPs.
While AWS Spot Instances are a great solution, consider expanding your spot instance strategy to include other cloud providers for added resilience:
Here are two major case studies explained and implemented:
Spot instances provide a powerful solution for Kubernetes clusters, combining cost savings with scalable resources. By following best practices, setting up proper node groups, and implementing autoscaling, you can effectively manage your Kubernetes workloads on spot instances.
Sedai’s Autonomous Optimization Platform brings an extra layer of resilience, enabling you to predict interruptions, optimize configurations, and maintain stability even with the unpredictable nature of spot instances. If you're looking to scale efficiently while minimizing costs, adopting advanced scheduling and scaling practices with Sedai is the key to success.
Schedule a demo today to see how Sedai can transform your Kubernetes operations and help you achieve a cost-effective, reliable environment with minimal manual intervention.
Spot instances in Kubernetes are discounted, with excess capacity offered by cloud providers like AWS, making them significantly cheaper than on-demand instances. They allow you to scale affordably, but they can be reclaimed by AWS with minimal notice. Learn more about maximizing cost-efficiency with spot instances on Sedai’s blog.
Sedai’s platform uses real-time monitoring and AI-driven optimization to predict interruptions, adjust configurations, and maintain stability across clusters using spot instances. For an in-depth look at Sedai’s autonomous optimization, check out this blog post.
Setting up Kubernetes on spot instances requires AWS IAM roles, permissions, and Kubernetes tools like kubectl and eksctl. Proper access to EC2 spot instances is essential for node group configuration. For more setup guidance, read through Sedai’s blog on Kubernetes configurations.
The Cluster Autoscaler automatically scales your cluster based on demand, allowing you to prioritize spot instances for cost savings. Sedai’s platform further enhances this by dynamically adjusting instance types to match real-time demand. Explore Sedai’s insights on autoscaling in their blog.
Spot instance interruptions are managed by tools like the AWS Node Termination Handler, which reschedules pods when instances are terminated. Sedai’s predictive analytics can help anticipate and manage these interruptions more proactively. Learn about handling interruptions on Sedai’s blog.
Best practices include diversifying instance types, using multiple availability zones, and balancing spot and on-demand instances. Sedai’s platform helps automate these practices by optimizing node group settings. Discover more best practices on Sedai’s blog.
Sedai’s platform manages pod scheduling by adjusting affinity and toleration settings, ensuring an even distribution of workloads. This helps minimize disruption during interruptions. For a detailed guide on Sedai’s scheduling solutions, visit their blog.
Tolerations and node affinity allow you to prioritize certain workloads on spot nodes while keeping critical workloads on more stable instances. Read more about managing workloads effectively on Sedai’s blog.
Use tools like kubectl for manual scaling or rely on Sedai’s platform, which dynamically scales applications based on real-time demand. This approach ensures stability without excessive costs. Explore Sedai’s scaling strategies in their blog.
Sedai’s platform continuously monitors, predicts, and optimizes configurations, balancing cost savings with reliability by minimizing manual interventions and optimizing for cost-efficiency. For more insights, check out Sedai’s blog.
