Kubernetes provides powerful orchestration capabilities but can become expensive without cost optimization. Over-provisioned resources waste money, while under-provisioning degrades performance. European organizations running Kubernetes clusters need systematic approaches to resource allocation, autoscaling configuration, and cost monitoring that balance efficiency with reliability.
Resource Request Tuning
Accurate resource requests ensure efficient cluster utilization without performance degradation. Resource requests that far exceed actual usage waste capacity, while insufficient requests cause throttling and performance issues. Historical usage data informs appropriate request values. Regular analysis identifies opportunities to right-size requests as application behavior evolves.
- Use Vertical Pod Autoscaler recommendations to guide resource request optimization
- Monitor actual resource usage versus requests to identify over-provisioned workloads
- Set appropriate resource limits to prevent individual pods from impacting cluster stability
- Implement quality of service classes that align with workload criticality
- Use node selectors and affinity rules to optimize workload placement
Autoscaling Strategies
Horizontal Pod Autoscaling adjusts replica counts based on load, optimizing resource usage dynamically. Cluster autoscaling adds or removes nodes to match capacity needs. Proper configuration prevents both resource shortages and excessive overhead. Metrics selection significantly impacts autoscaling effectiveness—CPU and memory work for some workloads while custom metrics better suit others.
Cost Visibility and Allocation
Understanding where costs accumulate enables targeted optimization. Tools like Kubecost provide granular cost visibility across namespaces, deployments, and applications. Chargeback mechanisms allocate costs to appropriate teams or products. Regular cost reviews identify expensive workloads and optimization opportunities. Cost awareness throughout organizations drives more efficient resource usage.