Kubernetes Cost Audit and Reduction#

Kubernetes clusters accumulate cost waste silently. Resource requests padded “just in case” during initial deployment never get revisited. Load balancers created for debugging stay running. PVCs from deleted applications persist. Over six months, a cluster originally running at $5,000/month can drift to $12,000 with no corresponding increase in actual workload.

This operational plan works through cost reduction systematically, starting with visibility (you cannot cut what you cannot see), moving through quick wins, then tackling the larger structural optimizations that require data collection and careful rollout.

Kubernetes FinOps: Decision Framework for Cost Optimization#

FinOps in Kubernetes is the practice of bringing financial accountability to infrastructure spending. The challenge is not a lack of cost-saving techniques – it is knowing which ones to apply first, which combinations work together, and which ones introduce risk that outweighs the savings. This article provides a structured decision framework for selecting and prioritizing Kubernetes cost optimization strategies.

The Five Optimization Levers#

Every Kubernetes cost optimization effort works across five levers. Each has a different risk profile, implementation effort, and savings ceiling.

Kubernetes Cost Optimization#

Most Kubernetes clusters run at 15-30% actual CPU utilization but are billed for the full provisioned capacity. The gap between what you reserve and what you use is pure waste. This article covers the practical workflow for finding and eliminating that waste.

The Cost Problem: Requests vs Actual Usage#

Kubernetes resource requests are the foundation of cost. When a pod requests 4 CPUs, the scheduler reserves 4 CPUs on a node regardless of whether the pod ever uses more than 0.1 CPU. The node is sized (and billed) based on what is reserved, not what is consumed.