Kubernetes clusters run at 10% average CPU utilization while companies pay for 100% of provisioned capacity

Organizations running Kubernetes in the cloud massively over-provision compute resources, with the Cast AI 2025 Kubernetes Cost Benchmark showing 99% of clusters carrying more capacity than they use, averaging just 10% CPU utilization and 23% memory utilization. This means 30-45% of all Kubernetes cluster spending is wasted on idle resources. Why it matters: engineering teams over-provision out of fear that right-sizing will cause outages, so finance teams cannot get accurate cloud cost forecasts, so CFOs impose blunt cost-cutting mandates on engineering, so engineers spend more time on cost optimization busywork than building product features, so the company's competitive velocity decreases while its cloud bill continues to grow 20-30% year over year. The structural root cause is that Kubernetes abstracts away the direct relationship between application resource needs and infrastructure costs — no single engineer owns the cost of their workloads, and the default Kubernetes scheduler optimizes for availability rather than efficiency, making waste the path of least resistance.