Guide
Kubernetes cost optimization tools: 9 options compared
By Muhammad Soliman, Founder at SeaGit · Verified September 2026 ·
Kubernetes cost optimization covers three different problems that get conflated. The first is node-level waste: clusters running more or larger nodes than workloads need because autoscalers are absent or tuned conservatively. Karpenter and Cluster Autoscaler address this by launching and terminating nodes to match demand; Karpenter selects instance types and bin-packs directly against cloud APIs, while Cluster Autoscaler resizes existing node groups. The second is pod-level waste: containers requesting far more CPU and memory than they use, which blocks bin-packing regardless of node autoscaling. Goldilocks, Robusta KRR, CAST AI and ScaleOps target this, ranging from a free dashboard that recommends requests to SaaS platforms that continuously rewrite them. The third is visibility: OpenCost and Kubecost show where the money went, by namespace, label or team. A separate lever, scheduling non-production clusters off outside working hours, is what platforms like SeaGit build into policy rather than a cron job. Infracost adds pre-deployment visibility, estimating what a Terraform change will cost before it runs.
Grouped by the problem each tool actually solves — node autoscaling, pod rightsizing, cost visibility, and pre-deployment estimation — because a single "best tool" comparison across all four is comparing things that do not compete with each other.
Key takeaways
- Most Kubernetes overspend is requests that are larger than usage, and nodes that are not full. Fix the requests first; a node autoscaler cannot remove capacity that pods still reserve.
- Measure before you change anything. OpenCost is the open-source project behind Kubecost, and it allocates cluster spend to namespaces, labels and workloads from your cloud billing data.
- Vertical Pod Autoscaler in Off mode only recommends values. Auto mode evicts pods to apply them, so use it with disruption budgets in place.
- Karpenter provisions nodes for pending pods and consolidates underused ones. The Cluster Autoscaler scales existing node groups. Both change the bill; they do different jobs.
- Cost tools cannot see savings you do not act on. Pick the tool that matches the change you will actually make, and review the numbers on a schedule.
At a glance
| Tool | Best for | Pricing model |
|---|---|---|
| OpenCost / Kubecost | Teams that want vendor-neutral, in-cluster cost allocation they can query themselves, or a free-to-install dashboard on top of it without adopting a SaaS optimizer. | OpenCost is free and open source (Apache 2.0, CNCF). Kubecost/IBM Kubecost is free to install for a single cluster; broader enterprise features are sold through Apptio/IBM sales. |
| CAST AI | Teams that want an outcome-based, mostly hands-off optimizer and are comfortable granting it write access to change live cluster state. | Usage/outcome-based: a percentage of realized savings plus a per-CPU fee, quoted directly by CAST AI. |
| ScaleOps | Teams with large, bursty workloads who want real-time request-tuning rather than a periodic manual review cycle. | Custom, quote-based pricing; no public tiers. |
| Karpenter | Teams on EKS who want fast, flexible node provisioning without hand-maintaining cloud-specific autoscaling groups. | Free and open source (Apache 2.0). No product to buy — the cost is the compute it provisions. |
| Cluster Autoscaler | Teams that want the long-established default autoscaler with broad support across cloud providers and cluster-api/on-prem setups, not just AWS. | Free and open source (Apache 2.0, kubernetes/autoscaler). |
| SeaGit | Teams whose cost lever is scheduling: action rules that scale node groups down or power a whole cluster off outside working hours, defined as policy rather than a hand-rolled cron job. | Flat per-seat platform fee with a free plan; your AWS account is billed directly for compute, with no infrastructure markup. |
| Goldilocks + VPA | Teams that want a free, visual starting point for right-sizing pod requests before deciding whether to adopt a paid automation product. | Free and open source (Apache 2.0). |
| Robusta KRR | Teams that want a one-off or CI-integrated rightsizing report rather than a standing controller watching the cluster continuously. | Free and open source (MIT). Built by Robusta, which also sells a paid Kubernetes observability and alerting platform. |
| Infracost | Teams that want cost visibility at the pull-request stage, before an infrastructure change ever runs. | The CLI is free and open source (Apache 2.0). Infracost Cloud, an optional SaaS dashboard for FinOps policy and guardrails, is sold on paid tiers. |
1. OpenCost / Kubecost
OpenCost is the CNCF open-source cost allocation engine for Kubernetes — CPU, memory, GPU and storage cost by pod, namespace or label, exported to Prometheus. Kubecost (rebranded IBM Kubecost after Apptio/IBM acquired it) wraps that same engine in a UI, multi-cluster views and governance features.
- Not for you if
- Allocation, not action: OpenCost tells you what something costs, it does not rightsize or reschedule anything. Kubecost’s multi-cluster and governance features sit behind IBM/Apptio’s paid, contact-sales enterprise tiers.
2. CAST AI
A SaaS platform that connects to a running cluster and continuously automates rightsizing, autoscaling, bin-packing and spot-instance use, rather than just reporting on them.
- Not for you if
- Proprietary SaaS priced as a percentage of the savings it generates plus a per-CPU fee — there is no public price list, and getting a number means a sales conversation. Because it actively modifies resource requests and node counts, teams need to monitor its changes, not just its savings report.
3. ScaleOps
A SaaS platform for continuous, application-aware rightsizing of pod CPU and memory requests plus cluster autoscaling automation, across EKS, GKE, AKS, OpenShift and self-managed clusters.
- Not for you if
- No published pricing — quote-only, contact sales. Like CAST AI, it is an automation layer that rewrites live pod resource requests, which some teams will want to phase in on non-critical namespaces first rather than trust cluster-wide from day one.
4. Karpenter
An open-source, Kubernetes SIG-hosted node-lifecycle autoscaler, originated by AWS, that provisions and terminates nodes directly against cloud APIs and consolidates workloads onto fewer, cheaper nodes.
- Not for you if
- Only optimizes the node layer — it does not touch pod-level resource requests, so badly-sized requests still waste money even with perfect node packing. It originated on AWS; the Azure provider is production-ready but GCP support is still community work.
5. Cluster Autoscaler
The original, cloud-provider-agnostic Kubernetes SIG project that resizes existing node groups up or down based on unschedulable pods and node utilization.
- Not for you if
- Coarser-grained than Karpenter: it scales node groups you already defined rather than choosing an optimal instance type on demand, so it typically captures less savings from workload-shaped provisioning.
6. SeaGit
Provisions and manages Amazon EKS clusters inside the customer’s own AWS account — Terraform-managed VPC and node groups, one-click add-ons (Argo CD, Argo CI, Prometheus, MinIO, cert-manager, external-dns), GitOps CD and ephemeral preview deployments.
- Not for you if
- SeaGit provisions AWS (Amazon EKS) today; Azure and GCP are on the roadmap, not available now. It does not do CAST AI- or ScaleOps-style live rightsizing or bin-packing — its cost lever is scheduling infrastructure off, not rewriting resource requests.
7. Goldilocks + VPA
Fairwinds’ open-source dashboard that runs the Kubernetes Vertical Pod Autoscaler (VPA) in recommendation mode across a namespace and shows suggested CPU/memory requests per workload.
- Not for you if
- Goldilocks only recommends — it does not apply changes. VPA can auto-apply recommendations, but doing so in production needs care, since resizing evicts and restarts the pod.
8. Robusta KRR
An open-source CLI that reads historical Prometheus usage and outputs recommended CPU/memory requests and limits, without installing a persistent controller in the cluster.
- Not for you if
- A point-in-time recommender, not a continuous controller — something else still has to apply the recommendations, and you re-run it periodically to catch drift.
9. Infracost
An open-source CLI that estimates the monthly cost of a Terraform plan before you apply it, matching resources — including EKS node groups — against a cloud pricing catalogue for AWS, Azure and GCP.
- Not for you if
- Estimates infrastructure-as-code changes, not live cluster utilization — it will not tell you a running pod is over-requesting memory, only what a Terraform diff will cost.
Vendor descriptions and pricing models were checked against each vendor's public site as of September 2026. Prices and limits change; the links above go to the current pages.
Where Kubernetes cost actually goes
The scheduler places pods using their resource requests, not their live usage. A node is full when the sum of requests on it reaches its allocatable capacity, even if the pods on it use a fraction of that. So a cluster can be billed for many nodes while CPU usage stays low. Two things drive the gap: requests set higher than the workload needs, usually because nobody measured, and fragmentation, where many nodes each have a little room left that no pending pod fits into.
Limits add a second source of confusion. A memory limit below real peak usage causes OOM kills, and a CPU limit below demand causes throttling that looks like slow code. Kubernetes documents requests and limits as separate settings with separate effects, and both belong in the review of any cost change.
Measure before you change anything
Start with the numbers you already have. The command below shows current usage per pod. It needs metrics-server installed in the cluster, and it shows a point-in-time reading, so run it at peak hours and again overnight. Compare the output with the requests in the manifests, which the next example shows.
kubectl top pods --namespace web --containers
kubectl top nodesRequests are set in the pod template. A container with explicit requests and a memory-only limit looks like this. Memory limits are the usual source of OOM kills, so set them from observed peaks plus headroom, not from a guess.
resources:
requests:
cpu: 250m
memory: 256Mi
limits:
memory: 512MiOpenCost turns that per-pod picture into spend. It reads cloud pricing and allocates node cost to namespaces and labels, so you can see which team owns the largest share. Kubecost builds on the same open-source core and adds a commercial product on top. Use it to decide where to start, then verify the allocation against the cloud bill, because allocation models are estimates.
Right-size the pods first
The Vertical Pod Autoscaler has three components: a recommender that computes target requests from history, an updater that evicts pods whose requests are far from target, and an admission controller that sets requests on new pods. Its updateMode decides how far it goes. Off produces recommendations you read and apply yourself. Auto applies them through eviction. A small set of workloads can run in Auto mode safely. Most production services should start in Off.
apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
name: web-vpa
namespace: web
spec:
targetRef:
apiVersion: apps/v1
kind: Deployment
name: web
updatePolicy:
updateMode: "Off"The VPA project lists known limitations, including incompatibility with workloads that set pod-level resources. Check that page before you enable it on a workload that uses that stanza, because the admission controller can then refuse to create the pod.
Right-size the nodes: Karpenter or Cluster Autoscaler
Node autoscaling decides the instance types and counts behind the requests. Cluster Autoscaler works inside node groups you define. It adds nodes to a group when pods are pending and removes empty or underused nodes after a delay. It is mature and it fits clusters with a few fixed instance types. Karpenter works from pending pods directly. It chooses an instance type that fits the pods, launches it, and consolidates nodes when their workload can move elsewhere. It needs more configuration on the AWS side.
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
name: default
spec:
template:
spec:
nodeClassRef:
group: karpenter.k8s.aws
kind: EC2NodeClass
name: default
requirements:
- key: karpenter.sh/capacity-type
operator: In
values: ["spot", "on-demand"]
disruption:
consolidationPolicy: WhenEmptyOrUnderutilized
consolidateAfter: 1mSpot capacity lowers the hourly price of nodes, and it can be reclaimed. Put interruption-tolerant workloads on a pool that allows Spot, and keep stateful or single-replica services on on-demand capacity. Read the Karpenter disruption settings before you let consolidation move a pod that has no second replica.
How to choose a cost tool
Match the tool to the decision. To find out who spends what, use OpenCost or Kubecost. To set requests, use the Vertical Pod Autoscaler in Off mode, with a dashboard that shows its recommendations. To change instance types and counts, use Karpenter on EKS or the Cluster Autoscaler on node groups. Infracost is a different category: it estimates the cost of Terraform changes before they apply, which catches waste before it is deployed. Commercial platforms such as CAST AI and ScaleOps combine several of these actions, and their pricing is set per vendor. Read each vendor’s current pricing page rather than a comparison table, including this one.
Expect a fixed cost too. Control plane fees, load balancers, NAT gateways and logging are billed outside the node pool, and no Kubernetes autoscaler reduces them. On EKS, the control plane fee depends on the Kubernetes version support tier, so an out-of-support cluster costs more to run. See the Terraform EKS guide on this site for the version lifecycle.
Where SeaGit fits
SeaGit creates clusters in your own AWS account, so the EC2, EBS and load balancer charges appear on your AWS bill with no SeaGit markup. The platform sets up the cluster, its node groups and its ingress. It is not a cost analytics product, and this guide does not claim it reports per-namespace allocation or runs rightsizing for you. Use OpenCost or a similar tool alongside it, and check the cluster and node settings in the docs before you change them.
SeaGit pricing: per-seat platform fee, with cloud bills paid to AWS
Sources
Checked as of October 2026.
- OpenCost documentation — what OpenCost allocates and how it reads cloud pricing
- Kubernetes: Resource Management for Pods and Containers — requests, limits and their scheduling effect
- Vertical Pod Autoscaler (kubernetes/autoscaler) — recommender, updater and admission controller; known limitations
- Karpenter documentation: NodePools — NodePool fields and consolidation
Read next
Frequently asked
What is the best tool for EKS cost optimization?
- It depends which layer is wasting money. If it is unused nodes, Karpenter is the fastest-moving option on EKS specifically, with Cluster Autoscaler as the more conservative, broadly-supported alternative. If it is oversized pod requests, start with the free Goldilocks/VPA combination or Robusta KRR before paying for CAST AI or ScaleOps to automate the same rightsizing continuously.
How do I reduce Kubernetes costs without buying a platform?
- Three free moves cover most of the easy savings: run Karpenter or Cluster Autoscaler so idle nodes are removed automatically, run Goldilocks or Robusta KRR to see which pods are over-requesting CPU and memory, and use OpenCost to see where the remaining spend is actually going by namespace or team before deciding what to fix next.
What is Kubernetes cost management, as distinct from cost optimization?
- Cost management is visibility and accountability — knowing what each team, namespace or workload costs, usually via OpenCost or Kubecost. Cost optimization is acting on that information — resizing requests, removing idle nodes, using spot capacity. You need management data to optimize well, but a cost dashboard alone does not reduce a bill by itself.
Is Kubecost free?
- The underlying engine, OpenCost, is free and open source under the CNCF. Kubecost itself — now IBM Kubecost after the Apptio/IBM acquisition — is free to install for a single cluster; multi-cluster views and governance features are sold as enterprise tiers through IBM/Apptio sales.
Does Karpenter replace Cluster Autoscaler?
- On EKS, most new deployments choose Karpenter over Cluster Autoscaler for its faster, instance-type-aware provisioning, but Cluster Autoscaler remains the safer default outside AWS or on cluster-api setups where Karpenter’s provider support is less mature. They solve the same problem differently rather than one strictly superseding the other.