Kubernetes Capacity Planning: How to Size a Cluster You Cannot Predict

Kubernetes capacity planning should distinguish actual resource use, pod requests, and provisioned node capacity because autoscalers respond primarily to requests rather than real consumption, allowing inflated requests to drive unnecessary node growth and low utilization. Citing Cast AI’s 2026 report, the guidance says requested CPU averages 69% above actual usage and recommends rightsizing workloads before calculating capacity, then maintaining roughly 15–30% headroom above peak corrected demand, adjusted for autoscaler response times and traffic-spike patterns. It differentiates short-term spike planning from longer-term growth and commitment planning, advises reserving 60–70% of a stable rightsized baseline through flexible spend-based cloud commitments while using Spot capacity for variable demand, and calls for separate treatment of stateful workloads because availability-zone-bound storage restricts pod mobility and requires dedicated pools and greater headroom. Non-production environments should be independently rightsized and scheduled down during idle periods, while plans should be checked regularly and revisited after major deployments, persistent autoscaler limits, rising OOM events, or changes in workload types. The text also presents automation, including Cast AI’s rightsizing, consolidation, and commitment-management tools, as a way to continuously reduce request inflation and improve utilization.

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