Analyze your Kubernetes cluster resource utilization and identify cost optimization opportunities. Get recommendations for rightsizing, spot instances, reserved capacity, and autoscaling strategies.
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Kubernetes workloads often run at 30-50% resource utilization, meaning up to half your cloud spend may be wasted. Our Kubernetes Cost Optimizer analyzes your cluster configuration and provides actionable recommendations to reduce costs by 20-70% through rightsizing, spot instances, reserved capacity, and efficient autoscaling strategies.
Kubernetes cost optimization involves aligning your cluster resources with actual workload demands. Over-provisioned clusters waste money on unused capacity, while under-provisioned clusters hurt performance. The key metrics are CPU and memory utilization - ideally targeting 60-80% to balance cost efficiency with headroom for traffic spikes.
Cost Savings Formula
Savings = Current Cost - (Optimized Nodes × Node Cost × Discount Rate)As container adoption increases, Kubernetes often becomes the largest line item in cloud bills. Unchecked, costs can spiral as teams provision resources without visibility into actual usage.
Developers typically request 2-5x more resources than needed 'just in case.' Without proper limits and requests, clusters run at 20-40% utilization while billing for 100%.
Cloud providers offer 50-80% discounts through spot instances, committed use discounts, and reserved capacity - but these require analysis to implement safely.
Production workloads often have predictable patterns. Understanding utilization helps you match node sizes and counts to real requirements.
Modern organizations require engineering teams to understand and manage their cloud spend. This calculator helps quantify optimization opportunities.
Dev/test environments often run 24/7 with minimal utilization. Right-size nodes and implement cluster autoscaler to scale down overnight and weekends.
Analyze production utilization patterns to identify over-provisioning. Use node pools with mixed instance types for cost-effective scaling.
Kubernetes jobs and batch workloads are ideal for spot instances - achieve 60-80% savings on interruptible workloads.
Shared clusters benefit from resource quotas and limit ranges. Proper namespace budgeting prevents any team from over-consuming resources.
Evaluate HPA, VPA, and Cluster Autoscaler settings to ensure pods and nodes scale efficiently with demand.
For stable baseline workloads, reserved instances or committed use discounts provide significant savings over on-demand pricing.
Target 60-70% for production workloads to maintain headroom for spikes. Development environments can safely run at 70-80%. Below 40% indicates significant waste; above 85% risks performance issues during traffic bursts.
Spot instances are safe for stateless, interruptible workloads with proper pod disruption budgets. Avoid for stateful services, single-replica deployments, or latency-sensitive applications. Mixed node pools (spot + on-demand) provide balance.
Use tools like kubectl top nodes, Prometheus/Grafana, cloud provider metrics (CloudWatch, Stack Driver), or dedicated platforms like Kubecost, cast.ai, or Spot.io for detailed utilization analysis.
Requests reserve guaranteed resources for scheduling. Limits cap maximum usage. Set requests to typical usage (for efficient scheduling) and limits to peak usage (to prevent noisy neighbors). The gap between them affects cluster efficiency.
Reserve capacity for predictable baseline workloads running 24/7 - typically 50-60% of your average usage. Use on-demand for variable traffic above baseline. 1-year commitments typically save 30-40%, 3-year save 50-60%.
Review monthly at minimum. Implement continuous monitoring with cost allocation tools. Set up alerts for spending anomalies and utilization thresholds. Regular rightsizing should be part of sprint planning.