5 Kubernetes Cost Myths That Are Costing You Money
Myth 1: Autoscaling Saves Money
HPA (Horizontal Pod Autoscaler) can actually increase costs if configured poorly. If your min replicas are too high, or scale-up thresholds too aggressive, you end up paying for capacity you don't need. We've seen cases where autoscaling costs 30% more than properly rightsized static deployments. The key: set min replicas based on actual minimum load, not peak.
Myth 2: Spot Instances Are Always Cheaper
Spot instances offer 60-90% savings per instance, but interruption-driven rescheduling adds overhead. If your workload isn't spot-friendly (stateful, long-running, latency-sensitive), the cost of disruption recovery can exceed the savings. Best for: batch jobs, CI/CD, dev environments. Worst for: databases, real-time APIs, singleton controllers.
Myth 3: Resource Limits Prevent Waste
Setting CPU limits doesn't save money — it throttles your pods. Setting memory limits causes OOM kills. The real cost lever is resource requests, which determine scheduling and node utilization. Optimize requests based on actual P95 usage, set limits only as safety nets (2x requests), and let pods use burst capacity when available.
Myth 4: Smaller Instances = Lower Costs
Running many small nodes often costs more than fewer large nodes due to system overhead. Each node reserves CPU/memory for kubelet, kube-proxy, and OS. On a t3.small (2 CPU), ~0.5 CPU goes to system pods — 25% waste. On a m5.4xlarge (16 CPU), system overhead is <5%. Larger nodes also improve bin packing efficiency.
Myth 5: Cost Monitoring Is Enough
Monitoring tells you what happened. It doesn't prevent the next cost spike. The most impactful cost optimization happens before deployment — at PR review time. Cloptima's PR cost impact analysis catches expensive changes before they reach production, which no monitoring tool can do.