Kubernetes FinOps Cost Optimization Guide (2026) | DevOpsNess

Kubernetes Cost Optimization for Teams: FinOps Tactics That Actually Work

Kubernetes cost issues are usually organizational before they are technical. Clusters hide spend behind shared infrastructure, and teams lose visibility into which workloads drive cost. FinOps for Kubernetes starts by making ownership and usage explicit.

Step 1: Allocate Cost by Namespace and Team #

Every workload should have labels for team, environment, and service.

Example: Required Labels #

yaml.yaml

metadata:
  labels:
    team: payments
    env: production
    service: checkout-api

Cost tools can map these labels to chargeback or showback reports.

Step 2: Right-Size Requests and Limits #

Over-provisioned requests create idle spend. Under-provisioned limits cause throttling and incidents.

Example: Before and After #

Before:

yaml.yaml

resources:
  requests:
    cpu: "1000m"
    memory: "2Gi"

After (based on p95 usage):

yaml.yaml

resources:
  requests:
    cpu: "250m"
    memory: "512Mi"
  limits:
    cpu: "500m"
    memory: "1Gi"

Multiply this across 100 pods and savings become material.

Step 3: Use Autoscaling with Guardrails #

HPA for stateless services with stable CPU or RPS metrics.
Cluster Autoscaler for node elasticity.
Pod Disruption Budgets for safe scale-down.

Example Policy: Block Oversized Requests #

yaml.yaml

apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
  name: enforce-resource-requests
spec:
  rules:
    - name: validate-cpu-request
      match:
        any:
          - resources:
              kinds: ["Deployment"]
      validate:
        message: "CPU request must be <= 1000m"
        pattern:
          spec:
            template:
              spec:
                containers:
                  - resources:
                      requests:
                        cpu: "<=1000m"