Kubernetes Cost Optimization: Cutting Your Cloud Bill Without Cutting Performance

The industry average Kubernetes cluster runs at about 35% CPU and 40% memory utilization. That means more than half of your Kubernetes compute spend is, at any given moment, not doing useful work.

Understanding Where the Waste Is

Resource request overprovisioning: Kubernetes schedules pods based on resource requests (what the pod claims it needs), not actual usage. Teams set high requests to ensure availability; the cluster fills up with reserved but unused capacity. Start by comparing resource requests to actual usage — the p95 CPU and memory usage for each service.

Node over-sizing: Running workloads on large instance types when smaller ones would suffice. Each node has fixed overhead (system pods, kubelet, OS) that represents a higher fraction of a small node’s capacity.

Always-on non-production environments: Dev and staging clusters that run 24/7 but are used 8 hours a day. Implement cluster scale-down on schedules (kube-downscaler, KEDA scheduled scaling) to shut down non-production environments outside working hours.

The Autoscaling Stack

Horizontal Pod Autoscaler (HPA): Scales the number of pod replicas based on CPU/memory metrics. Configure this on all production deployments.

Vertical Pod Autoscaler (VPA): Automatically adjusts resource requests based on actual usage. Run in recommendation mode first before enabling automatic changes.

Karpenter: Developed by AWS, now CNCF — significantly more responsive and cost-effective than the original Cluster Autoscaler, particularly for workloads with variable resource shapes.

Spot/Preemptible Instances

Running stateless workloads on spot instances typically reduces compute costs by 60-80% compared to on-demand pricing. With proper pod disruption budgets and graceful shutdown handling, the interruption risk is manageable.