Kubernetes Cost Optimization: 10 Strategies That Work

Kubernetes makes it easy to run a lot of software, and just as easy to pay for capacity nobody uses. Most clusters we review have significant waste, usually from over-provisioned resource requests and nodes that never scale down. These ten strategies are where we start.

1. Make costs visible first

You cannot optimize what you cannot see. Install a cost allocation tool such as OpenCost or Kubecost, and label workloads by team, service and environment. Share the numbers with the teams that own the workloads.

2. Right-size resource requests

Requests decide how much capacity the scheduler reserves. Teams often set them high "to be safe", which leaves nodes half empty. Compare requests with actual usage over a few weeks and bring them closer together. Tools like the Vertical Pod Autoscaler in recommendation mode make this easier.

3. Set sensible limits

Memory limits protect nodes from runaway pods. CPU limits are more nuanced, since they can cause throttling. Set memory requests and limits deliberately, and think carefully before applying strict CPU limits to latency-sensitive services.

4. Autoscale pods

Use the Horizontal Pod Autoscaler so services scale with load instead of running at peak capacity all day. For event-driven workloads, KEDA can scale on queue length and even down to zero.

5. Autoscale nodes

Pod autoscaling only saves money if nodes scale down too. Use Cluster Autoscaler or Karpenter, and check that nothing blocks scale-down, such as pods without disruption budgets or with local storage.

6. Use spot or preemptible capacity

Spot instances cost much less than on-demand capacity. They suit stateless services, batch jobs and CI runners that can handle interruption. Run a mix of spot and on-demand nodes, and spread replicas so a single reclaim does not take a service down.

7. Choose the right instance types

Pick node families that match your workload's CPU-to-memory ratio. Newer generations and ARM-based instances often give better price-performance. Fewer, larger nodes can also reduce per-node overhead.

8. Commit to your baseline

For capacity you run all the time, savings plans or committed-use discounts are significantly cheaper than on-demand pricing. Commit only to the steady baseline, and let autoscaling and spot cover the peaks.

9. Shut down what is not in use

Development and staging environments rarely need to run overnight or on weekends. Schedule them to scale down outside working hours, and use short-lived preview environments that are deleted when a pull request closes.

10. Clean up regularly

Orphaned load balancers, unattached volumes, old snapshots and forgotten namespaces add up. Run a monthly cleanup, and automate it where you can.

Where to start

If you only do three things: make costs visible, right-size requests, and make sure nodes actually scale down. Those usually deliver most of the savings with little risk.

Want a clear view of where your cluster spend goes? Book an infrastructure review.