6+ Strategies to Manage Kubernetes Clusters on Amazon EKS

Managing Kubernetes clusters on Amazon EKS requires addressing key challenges like scaling, resource allocation, security, and cost efficiency. By leveraging strategies such as Horizontal and Vertical Pod Autoscaling, EC2 Spot Instances, and IAM roles for secure access, you can optimize both performance and cost. Additionally, automating cluster maintenance and improving storage management ensures smooth operations without manual intervention. By following best practices for node provisioning and network policies, you can improve cluster performance while reducing unnecessary expenses.

Managing Kubernetes clusters on Amazon EKS becomes more complex as workloads grow, and this is now a common reality for engineering teams.

Kubernetes adoption has crossed 70% among enterprises in 2025, making production-grade cluster management a standard operational challenge rather than a specialized skill set. As clusters scale, teams often struggle with security gaps and rising cloud costs. 

Applying proven best practices across scaling, security, cost optimization, and performance tuning helps keep Amazon EKS environments efficient, resilient, and cost-effective as workloads grow.

In this blog, you’ll learn proven strategies to optimize your Kubernetes management on Amazon EKS, enabling you to improve scalability, improve security, and achieve cost-efficiency across your workloads.

Common Challenges Teams Face When Managing Kubernetes Clusters

Managing Kubernetes clusters at scale introduces several complex challenges that demand engineering expertise. Below are the most common issues you can encounter, along with practical solutions.

1. Complex Cluster Configuration

Configuring Kubernetes clusters becomes increasingly complex as teams scale, particularly when managing resource allocation and networking across multiple services.

Solution:

• Use Helm Charts: Standardize configurations across environments to ensure repeatable and predictable deployments.
• Audit Resource Requests and Limits: Regularly review and adjust resource allocations based on actual usage patterns. This prevents over- or under-provisioning, which can impact both performance and cost.

2. Upgrade and Version Management

Upgrading clusters and ensuring component compatibility can cause downtime or introduce vulnerabilities if not managed correctly.

Solution:

• Use Rolling Updates: Apply updates incrementally to avoid service disruption, leveraging Kubernetes’ built-in rolling update functionality.
• Stay Updated with Release Notes: Track deprecations and new features in Kubernetes releases. Use tools to replace outdated APIs before upgrades.

3. Cost Management

Inefficient scaling and unused resources in large Kubernetes environments can speed up cloud costs, especially without proper monitoring and optimization.

Solution:

• Go for Monitoring: Use tools like Sedai to track and analyze costs in real-time, breaking down spending by service or deployment to identify resource-heavy workloads.
• Set Up Cleanup Scripts: Automate the removal of unused resources, such as orphaned volumes, services, or deployments, to reduce waste and unnecessary expenses.

4. Dependency Management and Compatibility

Managing service dependencies and ensuring compatibility across clusters can lead to version conflicts and operational issues if not handled carefully.

Solution:

• Go for Service Mesh: Manage communication, security, and monitoring across microservices. A service mesh ensures consistent traffic routing, load balancing, and inter-service security.
• Use Kubernetes Operators: Automate deployment and management of stateful applications, maintaining consistency across clusters.

Understanding the common challenges of managing Kubernetes clusters helps explain why many teams turn to Amazon EKS for a more simplified solution.

Suggested Read: Kubernetes, Optimized: From Soft Savings to Real Node Reductions

Why Do Many Teams Choose Amazon EKS for Kubernetes?

Amazon Elastic Kubernetes Service (EKS) is a popular choice for teams running Kubernetes on AWS due to its strong features, smooth integration with AWS services, and strong scalability.

Here’s why teams often rely on EKS to manage their Kubernetes clusters:

1. Simplified Cluster Management

Managing Kubernetes clusters requires ongoing effort, particularly around scaling, upgrades, and patching.

EKS simplifies this process by managing the Kubernetes control plane and automating tasks such as patching and version upgrades. This allows teams to focus on worker nodes and application workloads, helping reduce overall operational overhead.

2. Smooth Integration with the AWS Ecosystem

Integrating Kubernetes clusters with AWS services can be complex and time-intensive. EKS provides native integration with core AWS services.

This tight integration enables teams to use AWS security, networking, and monitoring capabilities more easily, without extensive additional configuration.

3. High Availability and Fault Tolerance

High availability across multiple regions or Availability Zones is essential for production-grade workloads. EKS supports running Kubernetes clusters across multiple Availability Zones, enabling automatic failover and maintaining service continuity. This architecture helps minimize downtime and improve application fault tolerance.

4. Auto Scaling and Efficient Resource Management

Scaling Kubernetes clusters to match fluctuating workloads without over- or under-provisioning is difficult. EKS supports automatic scaling for both EC2 instances and Kubernetes pods based on real-time demand. This helps ensure efficient resource utilization while balancing performance and cost.

5. AWS-Backed Reliability and Support

Operating Kubernetes clusters on AWS requires reliable infrastructure and responsive support. With EKS, teams benefit from AWS’s enterprise-grade infrastructure and 24/7 support.

This foundation helps ensure consistent performance and provides dependable assistance for troubleshooting and optimization.

6. Continuous Updates and Compatibility

Keeping Kubernetes clusters up to date and compatible with new features can be time-consuming. EKS manages Kubernetes version upgrades automatically, ensuring clusters run stable, supported versions. This reduces the need for manual patching and helps maintain compatibility as Kubernetes evolves.

7. Reduced Operational Overhead

Manually managing Kubernetes clusters often introduces significant complexity, especially in large or distributed environments. By handling the Kubernetes control plane, EKS reduces this operational burden. As a result, your teams can spend more time on application development and less time maintaining infrastructure.

Once teams understand why Amazon EKS is a popular choice, the next step is learning how to optimize clusters to get the best performance and cost efficiency.

Also Read: Kubernetes Cost Optimization Guide 2025-26

8 Strategies to Optimize Kubernetes Clusters on Amazon EKS

Managing and optimizing Kubernetes clusters on Amazon EKS requires careful attention to scalability, resource utilization, security, and cost efficiency. The following actionable tips will help you get the most out of your Kubernetes environments on EKS:

1. Optimize Node Provisioning with Managed Node Groups

Managing EC2 instances manually can lead to inefficiencies and unused capacity. With EKS Managed Node Groups, AWS automatically handles provisioning, scaling, and lifecycle management of EC2 instances, reducing operational effort and improving consistency within your Kubernetes cluster.

How to Implement:

• Create Managed Node Groups: Use the EKS console to create managed node groups and select appropriate EC2 instance types (e.g., m5.large for general-purpose workloads).
• Enable Auto Scaling: Configure EC2 Auto Scaling to adjust node counts based on cluster demand.
• Audit Node Usage: Regularly track node health and performance, resizing instances as workloads change.

Tip: Segment node groups by workload risk profile so scaling events and instance interruptions never affect latency-sensitive and background workloads at the same time.

2. Set Up Horizontal Pod Autoscaling (HPA) and Vertical Pod Autoscaling (VPA)

Kubernetes resources are often underutilized or over-provisioned, leading to wasted compute or performance issues.

Horizontal and Vertical Pod Autoscaling (HPA and VPA) provide automated scaling that adjusts pod resources based on real-time usage and workload fluctuations.

How to Implement:

• Enable Horizontal Pod Autoscaling: Use kubectl autoscale to set scaling policies based on CPU or memory utilization.
• Set Vertical Pod Autoscaling: Apply VPA for workloads with fluctuating resource needs.
• Monitor Scaling: Regularly assess scaling performance and adjust thresholds as necessary.

Tip: Lock VPA to recommendation-only mode for critical services and feed those recommendations into planned changes rather than allowing live resource mutation.

3. Use EC2 Spot Instances for Cost Optimization

Running EKS clusters entirely on On-Demand instances can become expensive, especially for non-critical workloads.

EC2 Spot Instances provide access to unused EC2 capacity at significantly lower costs, offering a cost-effective solution for Kubernetes clusters.

How to Implement:

• Configure Spot Instances with Managed Node Groups: Use EKS Managed Node Groups to integrate Spot Instances alongside On-Demand nodes for efficient scaling.
• Diversify Spot Instance Usage: Use Spot Fleets across multiple instance types and Availability Zones.
• Enable Auto Healing: Set up EKS Auto Healing to automatically replace interrupted Spot Instances, minimizing disruption.

Tip: Limit Spot usage per service to a known failure tolerance so interruption rates never exceed what retry logic and SLOs can absorb.

4. Strengthen Cluster Security with IAM Roles for Service Accounts

Securing Kubernetes clusters becomes increasingly complex, particularly when managing access control and service credentials. IAM Roles for Service Accounts (IRSA) enable pods to securely access AWS resources without embedding static credentials.

How to Implement:

• Create IAM Roles for Service Accounts: Define IAM roles and attach policies tailored to specific Kubernetes service accounts.
• Assign IAM Roles to Pods: Annotate service accounts to enable role assumption.
• Enforce Least Privilege: Limit permissions to ensure each service account has only the necessary access.

Tip: Periodically compare live IRSA permissions against Terraform or Git definitions to catch privilege creep introduced during incidents or hotfixes.

5. Use Amazon EBS and EFS for Persistent Storage Optimization

Persistent storage management is crucial in Kubernetes, especially for stateful or high-throughput applications.

Amazon EBS provides high-performance block storage, while EFS offers scalable shared file storage, both of which integrate seamlessly with Kubernetes for persistent data management.

How to Implement:

• Use EBS for Block Storage: Attach EBS volumes to pods requiring low-latency, high-IOPS storage.
• Configure Amazon EFS for Shared Storage: Utilize EFS as a PersistentVolume for workloads needing shared file access across multiple pods.

Tip: Separate storage classes by performance tier and explicitly pin workloads to them to prevent silent performance degradation during node churn.

6. Automate Cluster Maintenance with AWS Systems Manager

Manual patching and upgrades increase the risk of downtime and vulnerabilities. Automating maintenance ensures consistent updates and reduces operational burden, keeping your Kubernetes clusters secure and up to date.

How to Implement:

• Automate Node Updates: Use AWS Systems Manager to create automation runbooks for node patching and cluster upgrades.
• Schedule Maintenance: Apply updates during defined windows to minimize production impact.
• Automate Node Group Updates: Enable automatic updates for EKS Managed Node Groups to maintain consistency.

Tip: Distribute node patching across Availability Zones to prevent compounding capacity loss during rolling updates.

7. Implement Network Policies for Pod-to-Pod Communication Control

As Kubernetes clusters grow, controlling pod-to-pod communication becomes essential for maintaining security and performance.

Kubernetes Network Policies enable you to restrict access and define communication boundaries between services within the cluster.

How to Implement:

• Define Network Policies: Set policies to control traffic flow between pods and limit access to sensitive services.
• Use CNI Plugins for Improved Networking: Utilize the AWS VPC CNI for optimized networking and better integration with AWS resources.
• Monitor Network Traffic: Regularly analyze traffic patterns to refine and enforce network policies.

Tip: Validate policies using intentional deny tests so enforcement failures surface immediately instead of during security incidents.

8. Manage Cluster Configuration with GitOps

Manual configuration changes in Kubernetes can lead to drift and inconsistencies. GitOps enforces configuration consistency by storing Kubernetes manifests in version control and automatically applying changes.

How to Implement:

• Deploy ArgoCD for GitOps: Use ArgoCD to synchronize Kubernetes configurations directly from Git repositories.
• Store Manifests in Git: Maintain all Kubernetes manifests, such as deployments and services, in version control.
• Automate Rollbacks: Use GitOps to roll back configurations when issues arise automatically.

Tip: Enforce pull-request approvals for production cluster paths only, allowing faster iteration elsewhere without weakening control where it matters.

Optimizing EKS clusters often brings security and compliance into focus, especially as environments scale and become more complex.

6 Ways to Improve Security and Stay Compliant in Kubernetes Environments

Security in Kubernetes is critical, especially as clusters scale and contain sensitive workloads. To ensure your Kubernetes environment is secure and compliant, you must implement best practices to minimize vulnerabilities and maintain regulatory compliance.

1. Maintain Compliance with Industry Standards

Compliance with regulatory frameworks such as GDPR, HIPAA, or SOC 2 is a core requirement when operating Kubernetes clusters. This is particularly in regulated industries where auditability and policy enforcement are critical.

How to implement:

• Automate Compliance Checks: Use tools to run automated compliance scans against CIS Kubernetes benchmarks.
• Continuous Compliance Monitoring: Use AWS Config or Azure Policy to enforce compliance policies across Kubernetes infrastructure and ensure configurations remain aligned with industry standards.

Tip: Treat compliance failures as deployment regressions by blocking releases that introduce new policy violations instead of remediating them post-deploy.

2. Secure Kubernetes API Server Access

The Kubernetes API server is a primary control plane component. If compromised, it can provide attackers with full administrative access to the cluster.

How to implement:

• Use Mutating Admission Controllers: Configure admission controllers, such as PodSecurityPolicy, to ensure only approved and secure configurations are allowed.
• Limit Public API Access: Restrict API server access to private networks or enforce secure access through VPNs.
• Enable API Auditing: Configure Kubernetes API auditing to log all API interactions, providing visibility and supporting forensic analysis if suspicious activity occurs.

Tip: Rotate and review API server access paths after every cluster upgrade, as defaults and exposed endpoints can change silently between Kubernetes versions.

3. Secure and Automate Secret Management

Exposed secrets, including API keys or database credentials, can lead to serious security incidents. Proper storage, rotation, and access control are essential to reducing risk.

How to implement:

• External Secret Management: Integrate with AWS Secrets Manager to store and manage sensitive data outside of Kubernetes.
• Automated Secret Rotation: Use automated rotation features, such as those in AWS Secrets Manager, to refresh credentials regularly without manual intervention.
• Use Kubernetes Secrets with Encryption: Enable etcd encryption and store secrets using Kubernetes Secrets to ensure data is encrypted both at rest and in transit.

Tip: Track secret access frequency and flag rarely used credentials, as unused secrets are often forgotten entry points rather than active dependencies.

4. Enforce Pod Security Standards

Insecure pod configurations, such as running containers with elevated privileges or root access, can introduce critical vulnerabilities into the cluster.

How to implement:

• Enable PodSecurity Admission (PSA): Use Kubernetes PodSecurity Admission to enforce predefined security profiles, including restricting privileged containers and enforcing non-root execution.
• PodSecurityPolicy (PSP) Enforcement: Apply PSPs to limit access to host namespaces and prevent privileged operations.
• Container Image Scanning: Integrate image scanning tools to detect vulnerabilities in container images before deployment.

Tip: Apply stricter pod security profiles only after confirming workload readiness, since premature enforcement often leads to teams bypassing controls instead of fixing configurations.

5. Automate Incident Response

Rapid detection and response are essential for limiting the impact of security incidents. Manual processes often introduce delays that increase risk exposure.

How to implement:

• Automate Responses with AWS Lambda: Configure Lambda functions to automatically respond to predefined security events, such as blocking suspicious IP addresses or isolating compromised pods.
• Integrate with SIEM Systems: Forward Kubernetes logs to SIEM platforms like Splunk or the ELK Stack for centralized analysis, enabling automated alerting and response workflows.

Tip: Regularly run controlled incident simulations to ensure automated responses still behave correctly as cluster topology and workloads change.

6. Regularly Update Kubernetes and Dependencies

Keeping Kubernetes components and dependencies up to date ensures that security patches are applied promptly, reducing exposure to known vulnerabilities.

How to implement:

• Automated Kubernetes Upgrades: Use EKS Managed Upgrades to keep the control plane and worker nodes aligned with the latest supported Kubernetes versions.
• Monitor Dependencies for Vulnerabilities: Continuously scan container images and application dependencies using tools, and correct issues before they reach production.

Tip: Maintain a short-lived “canary cluster” to validate upgrades and dependency changes under real workloads before touching long-lived production clusters.

How Sedai Helps Manage Kubernetes Clusters?

Managing Kubernetes clusters becomes increasingly complex as workloads grow. Traditional scaling mechanisms, like Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler, often struggle to respond effectively to real-time changes in demand.

This can lead to resource inefficiencies, performance slowdowns, and higher cloud costs. Sedai addresses these challenges through autonomous optimization that continuously adapts to workload behavior.

Using machine learning, Sedai dynamically adjusts pod and node resources based on actual demand, keeping Kubernetes environments responsive and cost-efficient without requiring constant manual tuning.

Here’s what Sedai offers:

1. Pod-Level Rightsizing (CPU and Memory)

Sedai continuously monitors real workload consumption and automatically adjusts pod resource requests and limits. This prevents both over-provisioning and resource starvation, enabling cost savings while improving application stability by aligning CPU and memory allocation with real usage patterns.

2. Node Pool and Instance-Type Optimization

By analyzing cluster-wide usage trends, Sedai identifies the most efficient node types for Kubernetes node pools. This reduces idle capacity, minimizes waste, and improves performance by ensuring nodes are properly sized and selected.

3. Autonomous Scaling Decisions

Instead of relying on static thresholds, Sedai uses live workload signals to make intelligent scaling decisions. This adaptive approach reduces failed customer interactions by scaling precisely according to real-time demand.

4. Automatic Remediation

Sedai proactively detects performance degradation, resource pressure, and pod instability before they impact applications. With automated remediation, engineering teams can achieve up to six times higher productivity, spending less time on firefighting and more time delivering value.

5. Full-Stack Cost and Performance Optimization

Sedai extends optimization beyond compute, covering storage, networking, and cloud commitment management. This ensures autoscaling operates efficiently across the entire cloud stack, delivering up to 50% cost savings while improving overall cloud performance.

6. Multi-Cluster and Multi-Cloud Support

Sedai supports Kubernetes environments across GKE, EKS, AKS, and on-prem clusters. A unified optimization engine provides consistency across environments, enabling teams to manage up to $3.5 million in cloud spend efficiently while scaling across multi-cloud architectures.

7. SLO-Driven Scaling

Sedai aligns scaling actions with defined Service Level Objectives (SLOs) and Service Level Indicators (SLIs), ensuring application reliability and performance remain stable even during sudden spikes in traffic or demand fluctuations.

With Sedai, Kubernetes clusters change into self-managing systems that automatically adapt to workload demands. By removing guesswork and minimizing manual intervention, Sedai helps teams maintain efficient, cost-effective, and secure Kubernetes environments at scale.

If you’re managing Kubernetes clusters with Sedai, use our ROI calculator to estimate how much you can save by reducing cross-cloud waste, improving cluster performance, and cutting manual tuning.

Must Read: Detect Unused & Orphaned Kubernetes Resources

Final Thoughts

Effective Kubernetes management on Amazon EKS is essential for maintaining the right balance between performance, scalability, and cost efficiency. As workloads expand and environments change, the need for continuous optimization becomes more evident.

Manual intervention has its limits, which is why automation plays a critical role. Platforms like Sedai analyze workload behavior in real time, identify resource requirements, and automatically apply optimization actions, ensuring smooth and efficient operations.

With Sedai, Kubernetes management on EKS becomes a dynamic, self-optimizing system where resources are continuously right-sized, costs are controlled, and you can focus more on innovation rather than ongoing maintenance tasks.

Start optimizing your EKS environment today, reduce unnecessary spend, and let your infrastructure work smarter.

FAQs

Q1. How do I optimize resource allocation in Kubernetes clusters on AWS?

A1. Resource allocation is best optimized by aligning pod requests and limits with real usage patterns. Review CPU and memory consumption regularly to ensure resources are distributed efficiently across nodes, avoiding both over-provisioning and under-provisioning that can affect performance and cost.

Q2. How can I improve the performance of my Kubernetes clusters on AWS?

A2. Cluster performance can be improved by using Horizontal and Vertical Pod Autoscaling to respond dynamically to workload changes. In addition, routinely assess resource utilization and fine-tune node sizes or pod configurations so infrastructure capacity closely matches application demands.

Q3. How can I maintain high availability for Kubernetes workloads across multiple AWS Availability Zones?

A3. High availability can be achieved by distributing Kubernetes workloads across multiple AWS Availability Zones. This approach helps keep applications running even if one zone encounters an outage, improving resilience through built-in redundancy and fault tolerance.

Q4. What are the key considerations when scaling Kubernetes clusters on AWS?

A4. When scaling Kubernetes clusters, both compute resources, such as EC2 instances, and networking components, including VPC configurations, must scale together. Ongoing performance monitoring is important to avoid inefficient scaling that may impact application responsiveness or drive unnecessary costs.

Q5. How do I handle security in large-scale Kubernetes clusters on AWS?

A5. Securing large-scale Kubernetes clusters requires strong role-based access control (RBAC) to limit resource access to authorized users only. Access permissions should be reviewed regularly, sensitive data should be encrypted, and network policies should be enforced to control pod-to-pod communication based on security needs.