Kubernetes has become the de facto standard for container orchestration, but its flexibility comes with a cost management challenge. Default configurations almost always lead to over-provisioning.
The single most impactful optimization. Most teams set resource requests based on peak usage plus a generous buffer. Analyzing actual utilization patterns typically reveals 40–60% over-provisioning.
Cluster autoscaler adjusts the number of nodes based on pending pods. Combined with Horizontal Pod Autoscaler, this ensures you’re only paying for capacity you’re actually using.
For fault-tolerant workloads, spot instances offer 60–90% savings over on-demand pricing. The key is designing for interruption with proper pod disruption budgets.
Not all workloads need the same instance type. Creating purpose-built node groups for different workload profiles (CPU-intensive, memory-intensive, GPU) avoids waste.
Namespace-level resource quotas prevent any single team from consuming disproportionate cluster resources. Combined with cost allocation, this creates accountability.
