Kubernetes Cost Optimization on AWS EKS

Running Kubernetes on AWS gives you flexibility—but costs can grow rapidly without the right guardrails. This post breaks down a pragmatic approach I've used to cut EKS costs while maintaining reliability and performance.

1) Choose the right node mix

• Use a mix of On-Demand for baseline and Spot for burstable workloads.
• Create separate node groups and use taints/tolerations to schedule appropriately.

2) Rightsize requests and limits

• Start with conservative requests and measure actual usage.
• Use VPA in recommend mode or tools like Goldilocks to guide requests.

3) Enable cluster autoscaling

• Leverage Karpenter or the Cluster Autoscaler.
• Consolidate nodes and scale to zero for non-prod workloads.

4) Observe and allocate

• Use Prometheus + Grafana and add cost allocation labels (team, service, env).
• Centralize logs with ELK/OpenSearch to find noisy workloads and anomalies.

5) Control egress and storage

• Prefer private networking, leverage S3 lifecycle policies, and rightsize EBS/GP3.

Results

Using the above, I've achieved 25–40% EKS cost reduction across multiple environments while preserving SLOs.