Last quarter, our AWS bill for Kubernetes infrastructure hit $18,000/month. Through systematic optimization, we reduced it to $10,800 without impacting performance.
The Problem
Our Kubernetes cluster was running 200+ microservices across 45 nodes. The bills kept growing, but we weren't sure where the money was going. Sound familiar?
Step 1: Resource Rightsizing
Most containers were over-provisioned. We found:
- CPU requests averaged 50% higher than actual usage
- Memory requests were 70% higher than needed
- Many pods had no resource limits set
We used Kubernetes Resource Recommender (VPA) to analyze actual usage patterns over 30 days. The results were eye-opening.
Step 2: Cluster Autoscaling
We enabled Cluster Autoscaler with these settings:
scale-down-delay-after-add: 10m
scale-down-unneeded-time: 10m
max-node-provision-time: 15mThis alone saved us $2,400/month by automatically removing unused nodes during low-traffic periods.
Step 3: Node Instance Optimization
We switched from on-demand to a mixed instance strategy:
- 30% On-Demand instances (for critical workloads)
- 70% Spot instances (for fault-tolerant workloads)
Spot instances saved us 60% on compute costs, but required implementing proper graceful shutdown handling.
The Results
| Optimization | Monthly Savings |
|---|---|
| Resource rightsizing | $3,600 |
| Cluster autoscaling | $2,400 |
| Spot instances | $1,200 |
| Total Savings | $7,200 |
Monitoring and Alerting
We set up alerts for:
- Node utilization below 40% for more than 1 hour
- Pods with CPU usage below 20% of requests
- Spot instance interruption rates above 5%
Lessons Learned
Cost optimization isn't a one-time activity—it's an ongoing process. We review our cluster efficiency monthly and adjust as needed.
The biggest lesson: start with observability. You can't optimize what you don't measure.