EKS Pricing Components, Examples, and 7 Ways to Cut Your Costs

Understanding the Components of EKS Costs 

Amazon EKS Cluster Pricing

EKS charges a per-cluster, per-hour fee based on the Kubernetes version's support tier:

• Standard support: For Kubernetes versions supported within 14 months of their release in EKS, the cost is $0.10 per cluster per hour.
• Extended support: For versions beyond standard support (up to an additional 12 months), the fee increases to $0.60 per cluster per hour. This is due to the operational complexity of supporting versions that are no longer in the Kubernetes project’s support window.

This pricing is in addition to the costs for the underlying compute and storage resources utilized by your workloads.

Amazon EKS Auto Mode

EKS Auto Mode automates cluster management, including infrastructure provisioning and scaling.

With Auto Mode, you pay based on the duration and type of Amazon EC2 instances managed by EKS Auto Mode. See the pricing examples below to get an idea of typical costs.

These charges are billed per second, with a one-minute minimum, and are in addition to the standard EC2 instance pricing.

Amazon EKS Hybrid Nodes Pricing

EKS Hybrid Nodes allow you to incorporate on-premises and edge infrastructure into your EKS clusters.

Pricing is based on the virtual CPU (vCPU) count of these nodes, charged per vCPU per hour. See the pricing examples below to get an idea of typical costs.

This enables unified Kubernetes management across diverse environments, with costs reflecting the resources reported by your nodes.

EKS Local Clusters on Outposts

For deploying EKS clusters on AWS Outposts, a fully managed service that extends AWS infrastructure to on-premises locations, pricing is $0.10 + and includes the cost of the Outposts rack configuration and any additional storage or compute resources.

Outposts are available for purchase with various payment options, including All Upfront, Partial Upfront, and No Upfront, over a three-year term.

Amazon EKS Pricing Examples

The examples below are correct for the US East region as of the time of this writing. For up-to-date pricing, see the official pricing page.

Example 1: Running an Amazon EKS Cluster for 20 Months

If you create an Amazon EKS cluster on a new Kubernetes version and keep it running without upgrading the control plane for 20 months, the cost changes based on the support type:

This scenario illustrates the transition from standard to extended support and highlights the impact of long-term use on costs.

Example 2: Deploying Amazon EKS Hybrid Nodes Across 30 Facilities

A research organization deploys Amazon EKS Hybrid Nodes to manage Kubernetes clusters for 30 research facilities globally. Each cluster consists of four nodes with six vCPUs per node.

The tiered pricing for vCPU-hours includes 400,000 hours at $0.02/vCPU/hour and 125,600 hours at $0.014/vCPU/hour. This setup ensures centralized Kubernetes management for the research facilities while optimizing cost efficiency.

Example 3: Using Amazon EKS Auto Mode for a New Application

A company deploys a containerized application using Amazon EKS Auto Mode, optimizing for reduced management effort and high availability.

This deployment leverages smaller instance types with a focus on cost optimization and efficient resource allocation. The high availability configuration ensures fault tolerance by distributing workloads across multiple instances. 

7 Strategies to Optimize Amazon EKS Costs 

1. Implement Auto Scaling Groups

Auto Scaling Groups (ASGs) optimize resource allocation in Amazon EKS. They automatically adjust the number of worker nodes based on the needs of Kubernetes applications. During peak traffic periods, ASGs can scale up nodes to handle the load, ensuring performance is maintained. During low-demand periods, the system can scale down, freeing unused resources.

To implement ASGs, it's crucial to configure scaling policies that reflect the application's behavior. Kubernetes integrates with ASGs using the cluster autoscaler, which adjusts node pools to meet pod demands. Configuring proper limits and thresholds for scaling prevents excessive or insufficient resource provisioning.

2. Utilize Spot Instances for Worker Nodes

Amazon EC2 spot instances provide a cost-effective way to manage worker node expenses, offering up to 90% discounts compared to on-demand instances. Spot instances utilize unused AWS capacity, making them ideal for workloads that can tolerate interruptions, such as batch processing or non-critical application components.

In an EKS environment, spot instances can be integrated into a cluster by using mixed node groups. This approach combines spot instances with on-demand or reserved instances, ensuring stability even if spot instances are reclaimed. Kubernetes' native pod rescheduling capabilities redistribute workloads, maintaining application performance.

3. Right-Size the Resources

Right-sizing resources involves selecting instance types and sizes that closely match the application's performance requirements without overprovisioning. Overallocated resources lead to unnecessary expenses, while underallocated ones can result in performance bottlenecks.

Kubernetes tools like the vertical pod autoscaler (VPA) and horizontal pod autoscaler (HPA) help ensure that pods use the appropriate amount of CPU and memory. The VPA adjusts resource requests and limits for pods, while the HPA scales the number of pods based on workload metrics such as CPU or memory usage. Regularly monitoring resource utilization with tools like CloudWatch or Prometheus can provide insights for further optimization.

4. Use Savings Plans and Reserved Instances

For workloads with predictable and consistent usage, AWS savings plans and reserved instances help reduce costs. Savings plans provide flexibility by applying discounts across different EC2 instance families, regions, and operating systems, while reserved instances require a commitment to specific instance configurations in exchange for higher discounts.

Savings plans are well-suited for organizations running diverse workloads where usage patterns may vary, as they adapt to changing resource needs. Reserved instances are suitable for stable, long-term workloads, such as databases or static web servers. Analyzing historical usage patterns and calculating cost forecasts using AWS Cost Explorer can help determine the best approach for the use case.

5. Implement Cost Allocation Tags

Cost allocation tags are a feature for tracking and managing AWS expenses across projects, teams, or environments. By assigning custom tags to resources like EKS clusters, EC2 instances, and EBS volumes, organizations can gain detailed insights into their spending. These tags enable cost attribution, making it easier to identify areas for optimization. However, due to the dynamic nature of Kubernetes environments, it can often be difficult to assign cost allocation tags and ensure they stay updated.

For example, tags such as "Environment: Production," "Team: DevOps," or "Project: MobileApp" can segment costs in AWS Cost Explorer or third-party tools like Kubecost. However, tags are only effective if teams can assign them consistently and update them as soon as changes occur in Kubernetes cluster structure, purpose, or ownership.

6. Use ECR Lifecycle Policies

Amazon Elastic Container Registry (ECR) lifecycle policies allow organizations to manage the lifecycle of container images stored in their registry. Over time, unused or outdated images can accumulate, leading to increased storage costs. Lifecycle policies automate the removal of these images based on user-defined rules, such as deleting images older than 30 days or keeping only the latest versions.

Implementing ECR lifecycle policies ensures that the registry remains clutter-free and cost-efficient. For example, admins might configure a policy to retain only the five most recent images for each tag, discarding older versions.

7. Leverage Cost Monitoring Tools

AWS offers several tools to help monitor and manage costs, such as Cost Explorer, AWS Budgets, and Cost Anomaly Detection. These tools provide visibility into spending patterns, allowing organizations to identify trends, track budget adherence, and receive alerts for unexpected cost increases.

Cost Explorer enables detailed cost analysis, breaking down expenses by service, region, or tags. AWS Budgets lets admins set custom thresholds and notify stakeholders when spending exceeds predefined limits. Cost Anomaly Detection uses machine learning to identify irregular cost patterns and prevent overspending. For deeper Kubernetes-specific insights, tools like Kubecost integrate with EKS for granular visibility into pod, namespace, and service-level costs. 

Learn more in our detailed guide to Kubernetes cost monitoring

Optimizing EKS Costs with Finout

Managing Kubernetes costs, particularly Amazon EKS, is a challenge for organizations running dynamic workloads at scale. EKS clusters often host multiple teams, applications, and environments, making cost attribution complex and resource allocation unclear. Finout simplifies this process by providing granular visibility into EKS expenses, breaking down costs by namespace, pod, or service. With Finout, organizations can track real-time usage, understand historical trends, and identify cost inefficiencies, ensuring that every dollar spent aligns with business priorities.

Beyond visibility, Finout enables precise cost allocation, allowing businesses to attribute expenses to specific teams or projects seamlessly. This empowers organizations to implement effective chargeback or showback models, driving accountability and cost-conscious decision-making. By combining Finout’s capabilities with the flexibility of usage-based pricing, enterprises can optimize their EKS environments, reduce wasted spend, and ensure scalability without compromising financial efficiency.