Kubernetes cost pptimization: a practical guide for DevOps teams | Harness Blog

Kubernetes cost optimization focuses on aligning reserved CPU and memory resources with actual workload usage, since inflated pod requests are billed even when capacity is unused and can be multiplied across many replicas. The main approaches are right-sizing requests and limits using observed demand, applying horizontal, vertical, and cluster-level autoscaling, using spot or preemptible instances for fault-tolerant workloads, and allocating shared-cluster costs by namespace through consistent labels. Common sources of waste include overprovisioned requests, idle non-production environments, orphaned storage volumes, unused node capacity, and abandoned workloads, while useful measures include cost per namespace and workload, idle and unallocated spending, and cost per unit of work. The discussion notes that Kubernetes scheduling and cloud billing each lack complete cost context, making attribution and actionable visibility important, and presents Harness Cost Management as a platform that combines cost data, optimization recommendations, idle-environment suspension, node packing, spot placement, and anomaly detection near deployment workflows. It also cites examples of ConsumerTrack reducing Kubernetes service onboarding effort through automation and Tyler Technologies achieving annualized savings by suspending inactive non-production infrastructure on demand.

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