Kubernetes Cost Optimization
Why Kubernetes Costs Are Hard to Manage
Kubernetes abstracts infrastructure, making it easy to overprovision. Studies show that 35-45% of Kubernetes spending is wasted on overprovisioned resources, idle pods, and misconfigured autoscaling. Unlike traditional VMs, K8s costs are distributed across shared nodes, making attribution to teams and workloads non-trivial.
Understanding K8s Cost Allocation
Cost allocation in Kubernetes requires mapping pod resource consumption (CPU, memory, storage, network) to actual cloud costs. This means combining node pricing (on-demand, spot, reserved) with resource usage metrics. Key concepts: resource requests vs limits, namespace-level budgets, and label-based cost grouping.
Rightsizing Workloads
Rightsizing means setting resource requests and limits that match actual usage. Overprovisioning wastes money; underprovisioning causes OOM kills and throttling. Best practice: monitor actual CPU and memory usage over 7-14 days, then set requests to P95 of actual usage and limits to 2x requests. Tools like Cloptima automate this analysis.
Cluster Autoscaling Strategies
Cluster autoscaler adds/removes nodes based on pending pods. Key optimizations: use multiple node pools with different instance types, enable scale-to-zero for dev/staging, set appropriate scale-down delays, and use priority-based expanders. Combine with Horizontal Pod Autoscaler (HPA) for workload-level scaling.
Leveraging Spot Instances
Spot/preemptible instances offer 60-90% savings over on-demand. Best candidates: stateless workloads, batch jobs, CI/CD runners, and dev environments. Use pod disruption budgets (PDBs) for graceful handling. Avoid for stateful workloads and latency-sensitive services without proper fallback mechanisms.
Namespace Cost Budgets
Set resource quotas per namespace to prevent uncontrolled spending. Implement LimitRanges for default container resource constraints. Use Cloptima's budget alerts to notify teams before they exceed namespace cost targets. Map namespaces to teams for automatic cost attribution.
How Cloptima Helps
Cloptima provides deep K8s cost intelligence: workload-level cost breakdown, AI-powered rightsizing recommendations based on actual usage, idle resource detection, team attribution via namespace mapping, and unique PR cost impact analysis that comments on GitHub PRs when workload definitions change. No other tool offers this combination.