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