Amazon EKS (Elastic Kubernetes Service) is one of the most popular ways to run Kubernetes in the cloud. It lets development teams focus on applications instead of managing the cluster control plane. But an EKS cluster that is badly designed or misconfigured leads to higher costs, outages and painful maintenance.
This article walks through the 10 most common mistakes teams make when running Kubernetes on AWS, together with recommendations on how to avoid them.
1. Oversized nodes and no scaling
One of the most frequent mistakes in running Kubernetes on AWS is overprovisioning compute. Many teams deploy an EKS cluster on powerful EC2 instances that run continuously, regardless of the actual load. The result is that a large share of the compute capacity sits unused while the company keeps paying for it. The fix is the Cluster Autoscaler, which adjusts the number of nodes automatically to match what the workloads currently need. For further cost optimization it is worth combining on-demand and spot instances, with spot capacity carrying the less critical services that tolerate interruption.
2. Missing resource requests and limits
If pod manifests carry no requests and limits for CPU and memory, you get overloaded nodes, an unstable system and scaling problems. Every pod should state clearly how many resources it needs and how much it is allowed to consume at most. Those values let the scheduler allocate capacity more efficiently and prevent nodes from being packed beyond what they can handle. Organizations should also apply central policies, for example LimitRange and ResourceQuota in every namespace, so that the values are enforced automatically.
3. Loosely scoped IAM design
Badly designed IAM policies are another source of risk. Applications often run with far broader permissions than they need, or several workloads share the same IAM role. That not only breaks the principle of least privilege, it also makes the setup harder to understand and to audit. The recommended solution is IAM Roles for Service Accounts (IRSA), where every application in the cluster runs under its own identity with precisely scoped permissions to AWS services.
4. Ignoring observability
Without properly configured monitoring and alerting there is no way to catch problems early or to predict performance bottlenecks. Many teams rely on CloudWatch alone, which is often not enough for what a Kubernetes cluster needs. We recommend deploying tools such as Prometheus and Grafana, which give you a deeper view of metrics at pod, node and application level. For alerting to be effective, integrate Alertmanager or CloudWatch Alarms.
5. Long-term logging with no retention rules
Logging without control means costs rise quickly and monitoring systems get flooded. The typical example is application logs stored in AWS CloudWatch indefinitely, with no retention policy set. Organizations should think carefully about which logs they need to keep, for how long and at what granularity. To bring the cost down, route logs through Fluent Bit into S3 and set a shorter retention in CloudWatch.
6. Leaving unused objects in place
After testing or a temporary deployment, unused namespaces, volumes, services and load balancers often stay behind in the cluster. Those resources remain active, which means they remain billable. On top of the cost, they add operational complexity. Audit resources regularly and automate the cleanup with scripts, cron jobs or steps in the CI/CD pipeline.
7. No clear deployment strategy
Deploying by hand with kubectl apply and no defined process leads to inconsistent versions, changes that are hard to trace and difficult debugging. Deployments should be standardized with tools such as Helm or Kustomize and driven through GitOps tools such as ArgoCD or Flux. A CI/CD pipeline belongs at the foundation of every Kubernetes strategy.
8. No tagging system
Without consistent tagging of AWS resources and Kubernetes objects, costs cannot be attributed to individual teams, projects or environments. That missing visibility blocks any FinOps approach. Every object should carry metadata (environment, team, application, for example) so that costs can be filtered in AWS Cost Explorer and responsibility allocated properly.
9. Architecture too complex for a simple case
Not every application needs a full Kubernetes cluster. Many services run more efficiently as serverless functions or on AWS Fargate or ECS. Overengineering raises operating costs, complexity and maintenance effort. Reassess the architecture regularly against current needs and the actual complexity of the solution.
10. Postponing regular updates
Running a cluster or workloads on old Kubernetes versions brings security risks, reduces compatibility with modern tooling and makes future upgrades harder. AWS retires support for older EKS versions on a regular schedule, so following the official roadmap and planning updates is essential. To keep things stable, test new versions in a staging environment first and only then roll them out to production.
Conclusion: avoid the mistakes that raise cost and risk
Kubernetes on AWS is a powerful tool, but without the right decisions it leads to complexity and high bills. Avoid the recurring mistakes, adopt a FinOps approach and use proven best practices. Sound IAM design, resource optimization, monitoring, standardized deployments and disciplined updates are the foundation of an efficient and sustainable Kubernetes setup on AWS.
Want to avoid the expensive mistakes in running Kubernetes on AWS? Get in touch, we are happy to advise.
