[DRAFT — AWAITING APPROVAL]
Kubernetes Cost Optimization
Requests, limits, bin-packing and autoscaling: where Kubernetes cost actually hides.
Kubernetes makes it easy to run workloads and equally easy to overpay for them. Most waste comes from a small number of patterns that are visible once you look at the right data.
Requests drive the bill
The scheduler places pods based on resource requests, not actual usage. Inflated requests leave nodes looking full while CPU and memory sit idle. Compare requests against observed usage over a representative period and right-size iteratively.
Common sources of waste
- Requests set once at launch and never revisited
- Over-provisioned node groups with poor bin-packing
- Idle non-production clusters running around the clock
- Orphaned volumes, load balancers and unused namespaces
- Expensive instance types used where general-purpose nodes would do
Autoscale at every layer
Combine Horizontal Pod Autoscaling for replica counts, Vertical Pod Autoscaler recommendations for sizing, and a node autoscaler such as Cluster Autoscaler or Karpenter for capacity. Make sure each layer has sensible minimums and maximums so scaling stays predictable.
Use spot and commitments deliberately
Interruptible capacity suits stateless, fault-tolerant workloads with proper disruption budgets. Steady baseline usage is a candidate for committed-use or savings plans. Keep critical stateful workloads on on-demand capacity unless they are designed for interruption.
Make cost visible per team
Consistent labels for team, service and environment let you allocate cluster cost with tools such as OpenCost or Kubecost. Teams optimize what they can see and own.