Kubernetes Cost Optimization
OhChimp reads your cluster read-only and finds idle nodes, oversized requests, and zombie load balancers, then measures lowered requests at list rates.
What OhChimp optimizes for Kubernetes
Some of the cost levers OhChimp checks for Kubernetes. Each one becomes a reviewable plan you approve before anything changes.
Underutilized nodes and loose packing
OhChimp checks each node's requested CPU and memory against its allocatable capacity. It flags nodes sitting under 30% on both as drain candidates and prices the wasted spend per node. It also reports cluster-wide packing efficiency so you can see where dropping node count or smaller instance types pays off.
Over-provisioned Deployments
With metrics-server installed, OhChimp compares each Deployment's requested CPU and memory against what its pods actually use. It flags anything running under 30% utilization. The plan proposes specific request values with 30% headroom and the monthly dollar delta across all replicas.
Spot-eligible stateless workloads
Stateless multi-replica Deployments are usually safe on spot or preemptible nodes. OhChimp identifies those candidates and prices the saving from your cloud's spot discount (AWS, GCP, and Azure model roughly 70% off), while leaving StatefulSets on on-demand where they belong.
Zombie services and redundant load balancers
Services with selectors but no backing pods still hold a provisioned cloud load balancer that bills every month. OhChimp finds those zombies and prices the recurring charge. It also spots multiple LoadBalancer services sharing port 80 or 443 that you can consolidate behind a single Ingress.
Storage class migration and unused volumes
OhChimp finds PVCs not mounted by any pod and prices them by storage class so you can reclaim them. On AWS it also flags gp2, io1, and io2 volumes that migrate to gp3 online via aws ec2 modify-volume, with no pod restart and a snapshot-based rollback in the plan.
Autoscaler and consolidation blockers
OhChimp reads your cluster-autoscaler and Karpenter settings for conservative scale-down thresholds and slow drain delays that leave idle nodes running. It also flags PodDisruptionBudgets set to block all voluntary disruptions, which quietly prevents node consolidation and spot termination.
Worked example
For example, say OhChimp finds two nodes packed under 30%, a fleet of gp2 volumes on an EKS cluster, and a handful of LoadBalancer services all exposing port 443.
It writes one plan to consolidate workloads off the idle nodes, one to migrate the volumes to gp3, and one to fold the load balancers behind a single Ingress. Each plan carries a confidence score and rollback steps. You press apply. OhChimp measures a Kubernetes saving from the CPU and memory requests of the Deployment, StatefulSet, or DaemonSet a plan names, and none of these three changes lowers those requests. So OhChimp cannot measure what they save, and each saving stays an estimate. A plan that right-sizes a Deployment's requests is the kind OhChimp can measure and mark VERIFIED.
Kubernetes cost optimization FAQ
How does OhChimp connect to my Kubernetes cluster?
You provide a read-only kubeconfig, or a cluster server URL plus a service-account token. OhChimp confirms access by listing one namespace, then reads cluster metadata such as workloads, services, nodes, PVCs, and metrics-server usage. It never stores your application data, secrets, or workload contents.
Does OhChimp need write access or see what runs in my pods?
No. OhChimp uses read-only access and reads resource specs, node capacity, and usage metrics. It does not read the contents of your containers, ConfigMaps, or Secrets, and it never stores application data, secrets, or workload contents.
What Kubernetes costs can OhChimp actually reduce?
Underutilized nodes and loose bin-packing, over-provisioned Deployment CPU and memory requests, stateless workloads that belong on spot or preemptible nodes, zombie LoadBalancer services and redundant load balancers you can fold behind one Ingress, unused PVCs and AWS gp2/io1/io2 volumes that should be gp3, and autoscaler, PodDisruptionBudget, and descheduler settings that block node consolidation.
Who applies the changes, and can they be rolled back?
You do. Each fix is a reviewable plan with a confidence score, a risk level, and rollback steps, including the exact kubectl or cloud commands to reverse it. Nothing moves until you click apply, and OhChimp applies the matching code and infrastructure changes together.
How are the savings verified, and how is it priced?
OhChimp measures one kind of Kubernetes saving today: a plan that lowers the CPU and memory requests of a named Deployment, StatefulSet, or DaemonSet that is still in the plan's namespace. It prices those requests at a standard compute rate once a day for 30 days, so the figure comes from the requests, and OhChimp does not read your cloud bill. The plan is marked VERIFIED once that figure sits at least 10% below its cost at apply on 3 consecutive checks after day 7. If OhChimp measured the workload through the whole window and its cost never came down, the plan is flagged not implemented. Node, volume, load balancer, spot, and commitment plans do not change those requests, so OhChimp cannot measure what they save, and their savings stay estimates. Pricing is flat monthly, OhChimp never takes a cut of your savings, and paid plans carry a year-one ROI guarantee that lets you request a refund of your first 12 months of subscription fees if the verified savings on your Costs tab fall short of them. Estimated savings do not count.
The pod-request right-sizing lever, walked through in full: Cut Kubernetes costs by right-sizing pod requests.
Related integrations
Teams running Kubernetes usually run these too. OhChimp finds the waste in each and drafts every fix as a plan you approve.
GitHub
Code and IaC cost anti-patterns, repo scanning
Vercel
Function invocations, bandwidth, and plan fit
PostgreSQL
Unused indexes, bloat, and cache efficiency