Building a Production-Ready EKS Cluster: The Complete 2026 Guide

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Running Kubernetes on AWS sounds straightforward until you actually try it. After years of building and breaking EKS clusters, I’ve learned that the difference between a hobby project and production-grade infrastructure comes down to making the right architectural decisions early.

This guide walks you through everything that matters when building an EKS cluster

Understanding Your Cluster Strategy

The first question everyone asks is how many clusters do I actually need? The answer shapes everything that follows, so let’s get it right.

Most teams should start with three separate clusters for dev, staging, and production. I know it sounds like overkill, especially when you’re just getting started, but hear me out. When something breaks in dev (and it will), you don’t want it taking down production. Each environment has different scaling needs, different security requirements, and different tolerances for risk.

Trying to share a single cluster across environments with namespace isolation might save you some money upfront, but it creates operational headaches that cost far more in the long run. The exception here is small teams with very simple applications where the overhead of multiple clusters genuinely outweighs the benefits.

For the cluster version, there’s a simple rule that works well run the second-newest Kubernetes version in production (what we call N-1), and use the latest version in your dev and staging environments. This gives you stability in production while letting you catch any issues with new versions before they impact real users. Plan to upgrade every six months rather than waiting until AWS forces your hand.

When it comes to sizing, resist the temptation to start big. Begin with 3–5 nodes running something like m6i.2xlarge or m6a.2xlarge instances. These give you 8 CPUs and 32GB of RAM per node, which is a sweet spot for most workloads. You’ll set up autoscaling (we’ll cover that later), so the cluster will grow as needed. Starting with 20 huge nodes “for future growth” just burns money while you’re still figuring things out.

Getting Networking Right

Networking is where most EKS problems originate. The good news is that getting it right isn’t actually that complicated if you follow some basic principles.

Your VPC needs to be set up with both private and public subnets across at least three availability zones. The private subnets are where your worker nodes live, running your applications away from direct internet access. The public subnets are where your load balancers sit, accepting traffic from the outside world. This separation is fundamental to a secure cluster.

Size your VPC with a /16 CIDR block, which gives you about 65,000 IP addresses. This might seem excessive, but here’s the thing: each node in your cluster consumes 30–50 IP addresses for pod networking. A 100-node cluster can easily eat through 5,000 IPs. Planning for this from the start is much easier than trying to expand your address space later.

A simple VPC structure looks like this: create your VPC with 10.0.0.0/16, then carve out six subnets. Three public subnets get 10.0.0.0/20, 10.0.16.0/20, and 10.0.32.0/20. Three private subnets get 10.0.48.0/20, 10.0.64.0/20, and 10.0.80.0/20. Each subnet gets about 4,000 IPs, which provides plenty of room to grow.

For the networking plugin, start with VPC CNI, which is AWS’s default. It integrates seamlessly with AWS networking, making each pod a first-class citizen in your VPC. This simplifies debugging and works perfectly with AWS features like Security Groups for Pods. The alternative, Cilium, offers some advanced features and better IP efficiency, but you should only consider it if you’re running into IP exhaustion or need advanced network policies. For most teams, VPC CNI is the right choice.

On the IPv6 question, stick with IPv4 unless you have a compelling reason to add the complexity of IPv6. The support is solid now, but most tools and teams are still IPv4-native, and there’s no pressing reason to change that in 2026.

Choosing Your Compute Strategy

The compute layer is where your applications actually run, so getting this right matters a lot for both performance and cost.

Always use AWS Managed Node Groups rather than self-managed nodes. AWS handles the undifferentiated heavy lifting like AMI updates, graceful node termination, and ASG lifecycle management. The operational overhead you save is worth far more than any minor advantages of self-managed nodes.

For instance types, m6a instances (AMD-based) offer excellent value for general workloads. If you’re willing to rebuild your container images for ARM architecture, m7g instances (AWS Graviton) provide 20–30% better price-performance. For memory-intensive applications like databases or caching layers, look at the r6a or r6i families. CPU-bound workloads like video processing benefit from c6a or c6i instances.

The beauty of modern EKS is that you don’t need to pick just one instance type. Karpenter, which we’ll discuss in detail shortly, can dynamically choose from a pool of instance types based on your workload requirements and cost optimization goals.

Spot instances deserve special attention because they can cut your compute costs by 60–90%. AWS can reclaim Spot instances with just two minutes of notice, but for the right workloads, this trade-off is absolutely worth it. Web servers with multiple replicas, background job processors, and development environments are all excellent candidates for Spot. Databases and any single-replica critical services should stay on regular On-Demand instances.

A solid production strategy uses about 40% On-Demand instances as a reliable baseline, with 60% Spot instances handling the rest. Karpenter automatically manages this mix and handles Spot interruptions gracefully by provisioning replacement capacity before terminating interrupted nodes.

Fargate offers serverless nodes where you don’t manage any infrastructure, but it costs 20–30% more than equivalent EC2 capacity. It makes sense for batch jobs that run occasionally, isolated workloads handling sensitive data, or simple development environments. For steady-state production workloads, regular nodes are almost always more cost-effective.

Autoscaling with Karpenter

Karpenter has fundamentally changed how we think about node autoscaling in Kubernetes. The old Cluster Autoscaler required you to pre-define Auto Scaling Groups with specific instance types. Karpenter takes a completely different approach by looking at pending pods, calculating exactly what resources they need, and provisioning the optimal instance type on the fly.

This means Karpenter can choose from dozens of instance types to find the cheapest available option that meets your requirements. It provisions nodes in about 45 seconds compared to 90+ seconds with the old approach. Even better, it continuously analyzes your cluster and automatically consolidates underutilized nodes, replacing them with smaller, cheaper instances. This consolidation feature alone can save 15–40% on compute costs without any manual intervention.

The key to making Karpenter work effectively is setting accurate resource requests on your pods. When you deploy a container, you need to tell Kubernetes how much CPU and memory it actually needs. Without this information, Karpenter has no way to optimize costs or pack pods efficiently onto nodes.

Look at your production metrics after running for a week or two, check the actual CPU and memory usage, and set your requests to match the 95th percentile of usage. Set limits to about 2x your requests to prevent any single pod from consuming all node resources. This discipline around resource requests is the foundation of efficient Kubernetes operation.

Securing Your Cluster

Security in EKS is not optional, and thankfully, the tools have gotten much better. Let’s walk through what actually matters.

Pod Identity is the new AWS feature for giving your pods permissions to access AWS services like S3 or DynamoDB. It replaces the older IRSA (IAM Roles for Service Accounts) system with something simpler and faster. If you’re building a new cluster in 2026, use Pod Identity. If you’re running existing infrastructure with IRSA already configured, there’s no urgent need to migrate, but consider it during your next major update.

Network policies control which pods can communicate with each other and with external services. By default, Kubernetes allows any pod to talk to any other pod, which is a security nightmare. Start by creating a default-deny policy for each namespace, then explicitly allow only the communication patterns your applications actually need. Your backend pods should only accept traffic from frontend pods, and only send traffic to database pods and approved external APIs. Everything else gets blocked.

Kubernetes has three levels of pod security: privileged (no restrictions), baseline (minimal restrictions), and restricted (heavily locked down). Production namespaces should enforce the restricted level, which prevents privileged containers, blocks host namespace access, requires running as non-root, and drops unnecessary Linux capabilities. Any pod that violates these rules will be automatically rejected.

For secrets management, Kubernetes Secrets are not actually encrypted, just base64-encoded. Anyone with cluster access can decode them trivially. The right approach in 2026 is using AWS Secrets Manager combined with the External Secrets Operator. Your actual secrets stay encrypted in AWS Secrets Manager, and the operator automatically syncs them into your cluster as needed. This keeps secrets out of your Git repository, enables proper rotation, and gives you audit logs of secret access.

Every container image should be scanned for vulnerabilities before deployment. Amazon ECR provides built-in scanning at no extra cost, which catches most issues. Configure your CI/CD pipeline to block deployments if critical vulnerabilities are found, and log medium or low severity issues for later remediation.

Monitoring What Matters

You cannot manage what you cannot measure, so comprehensive monitoring is not negotiable for production clusters.

Prometheus has become the de facto standard for metrics in Kubernetes environments. Deploy it using the Prometheus Operator, which gives you automatic service discovery and easy configuration. You should be collecting metrics on node CPU, memory, and disk usage, pod counts and restart rates, application request rates and error rates, and API response times. Grafana provides the visualization layer, with pre-built dashboards for common scenarios and the ability to create custom dashboards for your specific applications.

For logging, use Fluent Bit to collect logs from all your pods and ship them to CloudWatch or OpenSearch. The critical thing here is filtering logs intelligently to control costs. Keep all error logs because you’ll need them for troubleshooting. Sample about 10–20% of informational logs to give you visibility without overwhelming your logging system. Drop debug logs entirely unless they indicate errors. This filtering strategy typically cuts logging costs by 60–80% compared to keeping everything.

Distributed tracing with OpenTelemetry helps you understand request flow across your microservices. When a user reports a slow page load, tracing lets you see exactly which service or database query caused the slowdown. Instrument your service-to-service calls, database queries, and external API calls. Sample about 1–10% of successful traces to control costs, but capture 100% of traces that result in errors since those are the ones you need to debug.

Deploying Applications Safely

Modern EKS deployments should use GitOps, where your Git repository becomes the single source of truth for everything running in your cluster.

ArgoCD is the leading GitOps tool for Kubernetes. It provides a web UI where you can see everything deployed in your cluster, automatically syncs changes from Git, and makes rollbacks trivial. The audit trail alone is worth the setup effort since you can see exactly who deployed what and when.

Structure your Git repository with separate directories for infrastructure components (things like Karpenter and cert-manager that rarely change), platform services (monitoring, logging, and other shared services), and applications (your actual business logic that changes frequently). This separation makes it clear who owns what and prevents application deployments from accidentally affecting cluster infrastructure.

For deployment strategy, never push changes directly to 100% of your users. Canary deployments let you release to a small percentage of traffic first, monitor for errors, and gradually increase exposure. Argo Rollouts automates this process beautifully. It deploys your new version to 5% of traffic, monitors key metrics for 10 minutes, and either continues rolling out or automatically reverts if error rates spike. This catches problems before they affect all your users.

Optimizing Costs

EKS costs can spiral quickly, but there are proven strategies to keep them under control.

Graviton instances (AWS’s ARM-based processors) offer 20–30% better price-performance than equivalent Intel or AMD instances. You’ll need to rebuild your container images for ARM architecture, but most applications run without modification. The return on investment is excellent, typically paying back the migration effort within a month.

Enable Karpenter’s consolidation feature, and it will continuously look for opportunities to replace underutilized nodes with smaller, cheaper ones. This happens automatically in the background, with no impact on running workloads. The savings typically range from 15–40% depending on your workload patterns.

Mixing Spot and On-Demand instances gives you the best of both worlds. Keep 40% of your capacity as On-Demand for reliability, and use Spot for the remaining 60%. Karpenter handles Spot interruptions transparently, provisioning replacement capacity before terminating interrupted nodes. The 60–90% discount on Spot instances makes this well worth the minimal additional complexity.

Right-sizing your pods is often overlooked but has huge impact. Deploy the Vertical Pod Autoscaler in recommendation mode, let it analyze your workloads for two weeks, and apply its suggestions. Most teams find their pods are requesting 2–4x more resources than they actually use. Fixing this improves cluster efficiency and cuts costs significantly.

If you’re running multiple small clusters, consider consolidating them. One larger cluster is substantially cheaper than several small ones due to fixed costs like control plane charges and baseline node counts. A shared cluster works well for small teams with similar workloads, though you’ll want separate clusters if you have strict compliance requirements or vastly different scaling patterns.

Your Path to Production

Before calling your cluster production-ready, validate that you’ve covered the essentials. You need high availability with nodes spread across at least three availability zones, multiple replicas for all stateless applications, and health checks configured properly. Security requires network policies, secrets in AWS Secrets Manager rather than Kubernetes Secrets, and pod security standards enforced at the restricted level.

Your monitoring stack should collect metrics with Prometheus, centralize logs, implement distributed tracing for critical paths, and configure alerts for error rates and latency spikes. Make sure someone is on-call to respond to those alerts and has runbooks to follow.

For scalability, install Karpenter with appropriate provisioner configuration, set up Horizontal Pod Autoscalers for your applications, and validate that resource requests are set on all pods. Run load tests that exceed your expected peak traffic to verify scaling works under pressure.

Document your disaster recovery procedures and actually test them. Create a backup of your cluster state with Velero, delete the cluster, and verify you can rebuild and restore everything. This exercise will expose gaps in your documentation and give you confidence that you can recover from disasters.

Avoiding Common Pitfalls

Several mistakes show up repeatedly in EKS deployments. Starting with a VPC that’s too small is one of the most painful because expanding address space later is difficult and disruptive. Always use a /16 or larger CIDR block.

Deploying pods without resource requests prevents Karpenter from optimizing costs and often leads to pods being scheduled on inappropriately sized nodes. Make setting resource requests part of your deployment checklist.

Running all your nodes in a single availability zone might save on data transfer costs, but it means an AZ outage takes down your entire cluster. The reliability benefit of multi-AZ deployment outweighs the modest increase in network costs.

Many teams ignore costs until their AWS bill becomes scary. Set up cost allocation tags, monitor spending by namespace, and review costs monthly from day one. Prevention is much easier than retroactive optimization.

Finally, resist the urge to over-engineer for hypothetical future scale. Design for 10x your current requirements, not 1000x. You can always add complexity later when you actually need it, but removing unnecessary complexity is much harder.

Getting Started

Break your implementation into four weeks. In week one, design your VPC and subnets, create the EKS cluster, deploy managed node groups, and install Karpenter. Week two focuses on security: configure Pod Identity or IRSA, implement network policies, set up secrets management, and enforce pod security standards.

Week three is about observability and deployment automation. Install Prometheus and Grafana, configure centralized logging, deploy ArgoCD, and structure your GitOps repository. In week four, implement canary deployment strategies, document disaster recovery procedures, run load tests to validate scaling, and train your team on operations.

This phased approach gets you to production-ready quickly while building on a solid foundation at each step.

Wrapping Up

Building production-ready EKS in 2026 comes down to making informed trade-offs and avoiding unnecessary complexity. Start with simple, proven patterns managed node groups, VPC CNI networking, and standard security practices. Add advanced features like Cilium or multi-region deployments only when you have specific requirements that justify the additional complexity.

Focus on security from day one because retrofitting it later is painful. Invest in comprehensive monitoring because you can’t fix what you can’t see. Automate deployments with GitOps because manual operations don’t scale. Optimize costs continuously because prevention is easier than emergency cost-cutting.

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