Kubernetes Cost Optimization: How to Cut Your Cluster Bill

Kubernetes cost optimization is the practice of cutting what your clusters cost by fixing three things: over-requested pods, idle nodes, and workloads running on the wrong instance type. Most Kubernetes overspend is not exotic. It comes from developers setting CPU and memory requests far above what the container actually uses, which forces the scheduler to reserve capacity nobody consumes, which forces the autoscaler to add nodes you then pay for around the clock. Fix the requests and a large share of the bill goes with them.

The awkward part of Kubernetes cost is that your cloud bill does not speak Kubernetes. AWS bills you for EC2 instances. Google bills you for Compute Engine. Neither one knows that the node you are paying for is 60 percent idle because the payments team asked for 4 vCPUs and uses 0.4. That translation layer, from node cost to namespace cost, is the whole problem.

Why Kubernetes bills grow quietly

A cluster is a shared pool of machines. Teams deploy into it without ever seeing a price. The feedback loop that normally keeps spending honest, where the person spending the money sees the money, is completely broken by design. Add a horizontal pod autoscaler and a cluster autoscaler on top, and the system will faithfully buy more capacity to satisfy whatever requests it is handed, correct or not.

Three specific mechanics drive almost all of it:

• Request inflation. An engineer does not know what the service needs, so they guess high. Nothing breaks, so the guess is never revisited. The scheduler treats that request as reserved, whether the container uses it or not.
• Bin-packing waste. Even with honest requests, pods rarely tile perfectly onto node sizes. A node that is 70 percent allocated still costs 100 percent.
• Idle capacity. Non-production clusters run all night and all weekend. Dev, staging, and QA environments are often the cheapest thing to fix and the last thing anyone looks at.

The single highest-leverage fix: rightsize your requests

If you do one thing, do this. Compare each container's CPU and memory requests against its actual observed usage over a couple of weeks, and set the request near the real p95 rather than the number someone typed in a hurry.

Use requests, not limits, as your starting point. Requests are what the scheduler reserves and therefore what drives your node count and your bill. Limits are a ceiling that governs throttling and eviction behavior. Teams sometimes tune limits, see no change on the invoice, and conclude Kubernetes cost optimization does not work. It does. They tuned the wrong number.

A practical rule that holds up well:

• Set CPU requests near p95 of observed usage. CPU is compressible, so a brief overshoot causes throttling, not a crash.
• Set memory requests closer to the observed peak with real headroom. Memory is not compressible. If a container exceeds its memory limit it gets OOM killed, and a cheaper cluster that drops requests is not cheaper, it is broken.
• Re-check quarterly. Traffic patterns change and yesterday's right size is tomorrow's waste.

This is the same discipline as rightsizing virtual machines, applied one layer up. The difference is that in Kubernetes the savings compound, because every over-request is multiplied by the replica count.

Then: stop paying for nights and weekends

Production has to run. Development usually does not. Scaling non-production node pools to zero outside working hours is close to free money, and on a normal five day schedule it removes roughly two thirds of the hours you are billed for in those environments.

Do it with a scheduled job that scales the node pool down in the evening and back up in the morning, and make sure someone can override it when a release runs late. After you make a cut like this, it is worth having something that tells you within seconds if a service stops responding, because the failure mode of an aggressive schedule is a developer arriving to a dead environment at 8am and losing an hour.

Then: buy the baseline, do not rent it

Whatever capacity your clusters run every single day is baseline, and paying on-demand rates for baseline is the most expensive way to buy compute. Cover it with Savings Plans, Reserved Instances, or committed use discounts depending on your cloud, and leave the spiky top of the curve on demand.

Spot and preemptible instances are the other half of this. Stateless, replicated, restart-tolerant workloads such as batch jobs, CI runners, and many web front ends run perfectly well on spot capacity at a steep discount. Stateful databases and single-replica services do not. Be honest about which of yours is which.

Allocation: the part that makes it stick

Every optimization above is a one-time cleanup unless someone owns the number afterward. Cost allocation is what turns it into a practice. That means mapping cluster spend back to namespaces, labels, and teams, so the platform bill stops being one unexplained lump and becomes a set of team-level numbers with names attached.

Once a team can see that their namespace costs 9,400 dollars a month, the conversation changes without anyone having to police it. This is the core of cost allocation and showback, and it is the difference between a cost-cutting project and a FinOps practice.

What tools actually do this

Generic cloud cost tools show you the node bill. Kubernetes-native tools translate that into namespace and pod-level cost. You usually want both, because the cluster is not the whole bill.

If you want the container-level detail, start with OpenCost or Kubecost and its alternatives. If the question you actually need answered is "what is our total technology spend and where is it leaking," a platform that covers the full bill will serve you better. Our comparison of cloud cost management tools lays out which is which.

Frequently asked questions

How do I reduce Kubernetes costs?

Rightsize CPU and memory requests to match real observed usage, which is where most of the waste lives. Then scale non-production clusters to zero outside working hours, cover your daily baseline capacity with committed discounts instead of on-demand rates, and move restart-tolerant workloads to spot instances. Finally, allocate cluster cost by namespace so each team owns their number.

What is the difference between requests and limits in Kubernetes?

Requests are what the scheduler reserves for a container, and they determine how many nodes your cluster needs, so they drive your bill. Limits are the hard ceiling a container cannot exceed, and they govern throttling and out-of-memory kills. To reduce cost you tune requests. Tuning limits alone will not change what you pay.

Why is my Kubernetes cluster so expensive?

Almost always because reserved capacity far exceeds used capacity. Pods request more CPU and memory than they consume, the scheduler reserves it anyway, and the cluster autoscaler adds nodes to satisfy those reservations. You end up paying for machines that sit largely idle. Compare requested resources against actual usage and the gap is usually obvious.

Is Kubecost free?

Kubecost has a free Foundations tier that supports unlimited clusters up to 250 cores with 15-day metric retention. Beyond that, paid tiers are quoted by IBM, which acquired Kubecost in September 2024. OpenCost, the open-source project Kubecost is built on, is free under the Apache 2.0 license with no size limit.

How much can Kubernetes cost optimization save?

It depends entirely on how inflated your resource requests are. Clusters that have never been reviewed commonly show requested CPU running several times higher than actual usage, and closing that gap reduces node count directly. Add non-production scheduling and commitment coverage on top, and a neglected cluster typically has meaningful double-digit percentage savings available.

Where to start this week

Pull a report of requested versus actual CPU and memory for your top 20 workloads by cost. You will almost certainly find a handful of services requesting several times what they use, and fixing those few is worth more than a month of careful tuning everywhere else. Then put a schedule on your non-production node pools. Those two moves take days, not quarters, and they pay for whatever tool you use to find them.