Kubernetes Cost Basics

This page is a hands-on tour of where Kubernetes spend actually lands, with the commands and manifests to see each line item for yourself.

Prerequisites

• kubectl matched to your cluster (Kubernetes 1.36.2).
• Metrics Server installed so kubectl top works.
• Helm 3 for installing cost tooling later.
• Read access to your cloud billing console for load balancer, disk, and egress charges.

Quick check that metrics are flowing:

kubectl top nodes
kubectl top pods -A

If those return numbers, you can compare real usage against reservations.

Basic Examples

1. See a node's allocatable capacity

The allocatable figure, not the raw hardware, is what the scheduler can hand out.

kubectl describe node <node-name> | grep -A6 "Allocatable"

• Allocatable is total capacity minus reservations for the kubelet, OS, and eviction thresholds.
• Bin-packing works against allocatable, so this is your real budget per node.
• The gap between capacity and allocatable is normal system overhead you still pay for.
• Compare this across node types to find the best price per allocatable core.

2. Read a pod's requests and limits

Requests reserve capacity; limits cap it.

kubectl get pod <pod-name> -o jsonpath='{.spec.containers[*].resources}'

• Requests are subtracted from node allocatable whether or not the pod uses them.
• A missing memory request means the scheduler treats it as zero and can overcommit the node.
• Limits above requests allow bursting into shared headroom.
• No limits at all lets one pod starve neighbors.

3. Find the reserved-versus-used gap

This gap is idle headroom, and it is pure waste.

kubectl describe node <node-name> | grep -A8 "Allocated resources"

• The "Requests" column shows reserved capacity across all pods on the node.
• Compare it to kubectl top node to see actual burn.
• A node reserved at 95 percent but burning 30 percent is a right-sizing target.
• Consistent gaps across nodes signal cluster-wide over-requesting.

4. Count your load balancers

Each LoadBalancer service is a billed cloud load balancer.

kubectl get svc -A --field-selector spec.type=LoadBalancer

• Every row is typically one provisioned, hourly-billed load balancer.
• Many rows often means many teams each exposing services directly.
• Consolidating behind an Ingress or Gateway controller collapses these.
• Internal-only services rarely need a public load balancer at all.

5. Inspect persistent volume spend

Disks bill by provisioned size, not by bytes used.

kubectl get pvc -A -o custom-columns=NS:.metadata.namespace,NAME:.metadata.name,SIZE:.spec.resources.requests.storage

• A 100 GiB claim storing 5 GiB still bills for 100 GiB.
• Released volumes with a Retain policy keep billing after the pod is gone.
• Check the StorageClass tier - premium SSD costs far more than standard.
• Snapshots add their own storage line item.

6. Right-size a request from real usage

The core basic recipe: set requests near observed usage.

resources:
  requests:
    cpu: "150m"
    memory: "256Mi"
  limits:
    memory: "256Mi"

• Base the request on the p95 of real usage from kubectl top, not a guess.
• Set the memory limit equal to the memory request to avoid OOM surprises.
• Leave CPU without a hard limit so pods can burst into idle headroom.
• Tighter requests let more pods share each node.

7. Label workloads for cost attribution

Labels are what any cost tool groups spend by.

metadata:
  labels:
    team: payments
    app: checkout
    environment: prod

• Consistent labels turn a flat bill into per-team, per-app showback.
• Enforce them with a policy engine so untagged spend cannot hide.
• namespace, team, and environment are the highest-value dimensions.
• Without labels, OpenCost and Kubecost cannot split shared node cost.

Intermediate Examples

8. Cap a namespace with a ResourceQuota

Quotas stop one team from silently reserving the whole cluster.

apiVersion: v1
kind: ResourceQuota
metadata:
  name: team-quota
  namespace: payments
spec:
  hard:
    requests.cpu: "20"
    requests.memory: 40Gi
    limits.cpu: "40"
    limits.memory: 80Gi
    services.loadbalancers: "2"

• The quota caps total reserved CPU and memory for the namespace.
• Capping services.loadbalancers prevents surprise load balancer sprawl.
• Pods that would exceed the quota are rejected at admission.
• Pair quotas with a LimitRange so pods without explicit requests still get sane defaults.

9. Spot cross-zone traffic risk

Cross-zone egress is metered per gigabyte and easy to miss.

kubectl get pods -A -o wide | awk '{print $8}' | sort | uniq -c

• This groups pods by node, which maps to availability zones.
• Chatty services split across zones generate billable inter-zone transfer.
• Topology-aware routing can keep traffic in-zone when possible.
• Watch data-heavy paths like databases and message queues most closely.

10. Estimate a node's monthly cost floor

A node running 24x7 sets a baseline you pay regardless of load.

kubectl get nodes -o custom-columns=NAME:.metadata.name,TYPE:.metadata.labels.node\.kubernetes\.io/instance-type

• Multiply each instance type's hourly rate by roughly 730 hours per month.
• That product is your fixed floor before any autoscaling.
• Fewer, larger, well-packed nodes usually beat many small ones on price per core.
• This baseline is what spot and committed-use discounts reduce.

Stack versions: This page was written for Kubernetes 1.36.2, Docker Engine 29.6.1 (BuildKit default), containerd (CRI runtime on nodes), Helm 3, Compose v2, Argo CD (latest - verify at build), and Gateway API (GA - verify controller support at build).

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