Kubernetes Cost Optimization: 7 Proven Strategies for 2026

on Kubernetes, Cloud, Devops, Cost optimization, Finops



Kubernetes Cost Optimization: 7 Proven Strategies for 2026

Running Kubernetes in production is powerful, but it can quickly become expensive if not managed properly. In this comprehensive guide, we’ll explore seven proven strategies to optimize your Kubernetes costs without sacrificing performance or reliability.

Kubernetes Infrastructure Photo by Taylor Vick on Unsplash

1. Right-Sizing Your Resources

The most common source of Kubernetes waste is over-provisioned resources. Many teams set CPU and memory requests based on guesswork rather than actual usage.

Implementing Resource Requests and Limits

resources:
  requests:
    memory: "256Mi"
    cpu: "250m"
  limits:
    memory: "512Mi"
    cpu: "500m"

Tools for Right-Sizing

  • Vertical Pod Autoscaler (VPA): Automatically adjusts resource requests
  • Goldilocks: Provides recommendations based on actual usage
  • Kubecost: Real-time cost monitoring and optimization suggestions

2. Leverage Spot/Preemptible Instances

Spot instances can reduce compute costs by 60-90%. The key is designing your workloads to handle interruptions gracefully.

apiVersion: v1
kind: Pod
metadata:
  name: spot-tolerant-pod
spec:
  nodeSelector:
    cloud.google.com/gke-spot: "true"
  tolerations:
  - key: "cloud.google.com/gke-spot"
    operator: "Equal"
    value: "true"
    effect: "NoSchedule"

Best Practices for Spot Instances

  • Use for stateless workloads
  • Implement graceful shutdown handlers
  • Spread across multiple availability zones
  • Combine with on-demand instances for critical workloads

3. Cluster Autoscaling Configuration

Proper autoscaling configuration prevents both over-provisioning and performance issues.

Cloud Cost Management Photo by Luke Chesser on Unsplash

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

4. Implement Pod Disruption Budgets

PDBs ensure high availability while allowing the cluster autoscaler to remove underutilized nodes.

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: app-pdb
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: my-app

5. Optimize Storage Costs

Storage costs often fly under the radar. Consider these strategies:

  • Use appropriate storage classes: Don’t use premium SSD for logs
  • Implement data lifecycle policies: Auto-delete old data
  • Compress data: Use compression for backups and archives
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: standard-resize
provisioner: kubernetes.io/gce-pd
parameters:
  type: pd-standard
allowVolumeExpansion: true
reclaimPolicy: Delete

6. Namespace Resource Quotas

Prevent runaway costs with namespace-level quotas:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: team-quota
  namespace: team-a
spec:
  hard:
    requests.cpu: "20"
    requests.memory: 40Gi
    limits.cpu: "40"
    limits.memory: 80Gi
    pods: "50"

7. Schedule Non-Critical Workloads Off-Peak

Use Kubernetes CronJobs and node affinity to run batch jobs during off-peak hours when spot prices are lower.

apiVersion: batch/v1
kind: CronJob
metadata:
  name: nightly-batch
spec:
  schedule: "0 2 * * *"  # 2 AM
  jobTemplate:
    spec:
      template:
        spec:
          nodeSelector:
            workload-type: batch

Cost Monitoring Dashboard

Implement real-time cost visibility with tools like:

  1. Kubecost - Open-source cost monitoring
  2. OpenCost - CNCF sandbox project
  3. Cloud provider tools - AWS Cost Explorer, GCP Cost Management

Conclusion

Kubernetes cost optimization is an ongoing process, not a one-time fix. By implementing these seven strategies, you can significantly reduce your cloud spend while maintaining the performance and reliability your applications need.

Key Takeaways:

  • Right-size resources based on actual usage
  • Use spot instances for fault-tolerant workloads
  • Configure autoscaling appropriately
  • Monitor costs continuously

Start with one strategy, measure the impact, and iterate. Your finance team will thank you.

What cost optimization strategies have worked for your Kubernetes clusters? Share your experiences in the comments below.

이 글이 도움이 되셨다면 공감 및 광고 클릭을 부탁드립니다 :)