How I Reduced My Kubernetes Cluster Cost by 60% Without Sacrificing Performance

Introduction

Running Kubernetes in production is powerful — but the bills can spiral fast.
When I first deployed my workloads, my monthly cloud cost felt like a second rent payment.

After months of tweaking, I managed to cut my Kubernetes cluster cost by ~60% — without compromising on performance or availability.

In this post, I'll walk through the exact techniques that worked for me.

1. Right-Size Your Resource Requests and Limits

Most engineers set CPU and memory requests based on guesswork.
This leads to over-provisioning — the silent killer of cloud budgets.

What I did:

• Used kubectl top pods and Prometheus metrics for real usage data
• Reduced over-allocated requests by analyzing 7-day P95 usage
• Set realistic limits (not 10x the request)

Tools that helped:

• Goldilocks
• Vertical Pod Autoscaler (VPA)

💡 Lesson: Requests should reflect reality, not paranoia.

2. Use Spot / Preemptible Nodes for Non-Critical Workloads

Spot instances can be 70-90% cheaper than on-demand nodes.

What worked for me:

• Moved batch jobs, dev environments, and CI runners to spot nodes
• Kept critical services on on-demand nodes
• Used node taints + tolerations to control scheduling

Example tolerations:

`yaml
tolerations:

• key: "spot" operator: "Equal" value: "true" effect: "NoSchedule" `

3. Implement Horizontal Pod Autoscaling (HPA) Properly

Without HPA, you're either over-paying or under-serving.

My setup:

• HPA based on CPU + custom metrics (request latency, queue depth)
• Min replicas: 2, Max: 10
• Scale-up fast, scale-down slow (avoid flapping)

4. Use Cluster Autoscaler with Smart Node Pools

A cluster running 24/7 with idle nodes is wasted money.

What I configured:

• Cluster Autoscaler with min/max node settings
• Separate node pools for memory-intensive vs CPU-intensive workloads
• Aggressive scale-down policies during off-peak hours

5. Schedule Non-Production Clusters to Sleep

Dev/staging environments don't need to run on weekends.

Tools I used:

• kube-downscaler
• Cron jobs to scale deployments to 0 at night

Saved: ~40% on dev cluster costs alone.

6. Optimize Persistent Volume Costs

Storage is sneaky — it accumulates fast.

My fixes:

• Deleted unused PVCs and orphaned PVs
• Switched cold data to cheaper storage classes
• Set up retention policies on logs and metrics

7. Monitor Costs with Open-Source Tools

You can't optimize what you don't measure.

Tools I recommend:

• Kubecost — visualizes cost per namespace/pod
• OpenCost — CNCF-backed alternative
• Cloud-native billing dashboards (AWS Cost Explorer, GCP Billing)

Final Results

Total savings: ~60% 🎉

Conclusion

Kubernetes cost optimization isn't about cutting corners — it's about using resources intelligently.
Start small: right-size your pods, add autoscaling, and monitor everything.

I've documented my full journey in detail on my blog if you want a deeper dive.

👉 Read the full breakdown on my blog

What's your biggest Kubernetes cost challenge?

Drop a comment — I'd love to hear what's worked (or failed) for you. 👇

Originally published at pratikshinde.online