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 podsand 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:
💡 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
| Metric | Before | After |
|---|---|---|
| Monthly Cost | $1,200 | $480 |
| CPU Utilization | 22% | 65% |
| Memory Utilization | 35% | 70% |
| Pod Restart Rate | High | Stable |
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
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