Kubernetes Cost Reduction: 40% Less Cloud Spend

The Problem

An e-commerce platform was spending $18,000/month on an AWS EKS cluster. The infrastructure team knew costs were high, but couldn’t identify where the waste was. No autoscaling was configured. Nodes ran at 20% utilization. Reserved instances weren’t in use. Nobody had a clear picture of cost per workload.

The Challenge

Cutting costs sounds simple, but the real task was finding waste without breaking anything:

• Thousands of running pods across multiple namespaces
• No cost allocation by team or service
• Fear of performance degradation during optimization
• Need for quick wins and long-term improvements

The Solution

We conducted a structured cost audit and optimization:

Phase 1: Cost Analysis

• Analyzed 60 days of CloudWatch and EKS metrics
• Mapped running pods to EC2 instance costs
• Identified waste: unused node pools, over-provisioned requests, idle PVCs
• Found 40% of cluster capacity was unused

Phase 2: Rightsizing

• Reduced node pool from 20 nodes to 14 nodes (same workload)
• Right-sized instance types (m5.xlarge → t3.large for non-critical workloads)
• Removed unused node pools for old services
• Consolidated workloads to reduce fragmentation

Phase 3: Autoscaling

• Enabled Cluster Autoscaler with proper node templates
• Configured KEDA for application autoscaling during traffic peaks
• Set resource requests/limits correctly (was over-provisioned by 60%)
• Implemented pod disruption budgets for graceful scaling down

Phase 4: Reserved Instances & Spot

• Purchased Reserved Instances for baseline capacity (30% discount)
• Enabled Spot instances for non-critical workloads (60-70% discount)
• Set up Spot Fleet diversification to reduce interruption risk
• Monitored Spot fallback for emergency scaling

Results

• 40% cost reduction: from $18,000 to ~$10,800/month
• Same workload throughput: no performance degradation
• Faster deployments: better autoscaling responds quicker
• Team visibility: clear cost attribution per namespace
• Operational confidence: documented optimization procedures

The company saved $86,400 annually without changing application code or reducing availability.

Key Takeaways

1. Most Kubernetes waste comes from over-provisioning, request tuning alone saves 20-30%
2. Spot instances are safe, with proper fallback and disruption budgets
3. Cost visibility matters, teams make better decisions with real cost data
4. Autoscaling only works with correct requests, set them based on observed usage, not guesses