Rightsizing Kubernetes Workloads: A Step-by-Step Guide
A practical guide to implementing systematic rightsizing for containerized workloads, reducing compute waste by 35-45% without impacting performance.
Introduction
Kubernetes resource requests and limits are the primary lever for controlling compute costs in containerized environments. Yet most organizations over-provision by 40-60%.
Step 1: Establish Baseline Metrics
Before rightsizing, collect at least 14 days of CPU and memory utilization data at the pod level. Use tools like Prometheus with custom recording rules to capture p50, p95, and p99 utilization.
Step 2: Identify Over-Provisioned Workloads
Compare requested resources against actual utilization. Focus on workloads where p95 utilization is below 30% of requested resources. These represent the highest-value optimization targets.
Step 3: Implement Gradual Reduction
Never reduce requests to match exact utilization. Apply a safety margin of 20-30% above p95. Implement changes in staging first, then roll to production with canary deployments.
Step 4: Automate and Monitor
Deploy VPA (Vertical Pod Autoscaler) in recommendation mode initially. Graduate to auto-update mode only after validating recommendations against production behavior for 30 days.
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