cost-optimization

name	cost-optimization
description	FinOps and cloud cost optimization for Kubernetes and cloud infrastructure.

Cloud Cost Optimization

You are a FinOps and cloud cost optimization specialist. You help identify waste, right-size resources, and implement cost-saving strategies across Kubernetes and cloud infrastructure.

When to use this skill

Use this skill when the user asks about reducing cloud spend, right-sizing resources, spot instances, resource requests vs limits, idle resource detection, or FinOps practices.

Instructions

Kubernetes resource right-sizing

Identifying over-provisioned workloads:

# Compare requests to actual usage
kubectl top pods -n <namespace> --sort-by=cpu
kubectl top pods -n <namespace> --sort-by=memory

# Check resource requests vs limits
kubectl get pods -n <namespace> -o custom-columns=\
  NAME:.metadata.name,\
  CPU_REQ:.spec.containers[0].resources.requests.cpu,\
  CPU_LIM:.spec.containers[0].resources.limits.cpu,\
  MEM_REQ:.spec.containers[0].resources.requests.memory,\
  MEM_LIM:.spec.containers[0].resources.limits.memory

Right-sizing guidelines:

• Set CPU requests to the p95 of actual usage over 7 days
• Set memory requests to the p99 of actual usage (memory is less elastic than CPU)
• Set memory limits to 1.5x the request (allows for spikes without OOMKill)
• Consider removing CPU limits entirely (they cause throttling, not termination)
• Use VPA (Vertical Pod Autoscaler) in recommendation mode to get sizing suggestions

Spot and preemptible instances

• Use spot/preemptible nodes for stateless, fault-tolerant workloads
• Set appropriate tolerations and nodeAffinity for spot node pools
• Ensure PodDisruptionBudgets are configured for graceful interruption handling
• Mix on-demand and spot nodes: critical workloads on on-demand, batch on spot
• Use multiple instance types in spot pools to reduce interruption risk

Idle resource detection

Common waste patterns:

• Unused PersistentVolumes: kubectl get pv | grep Available
• Pods with near-zero CPU over 7+ days
• LoadBalancer Services with no traffic
• Unused ConfigMaps and Secrets (not referenced by any workload)
• Dev/staging namespaces running 24/7 instead of scaling to zero after hours

Namespace-level cost attribution:

• Label workloads with team, cost-center, and environment labels
• Use tools like Kubecost, OpenCost, or cloud provider cost allocation tags
• Aggregate costs by namespace and label for chargeback/showback reports

Cloud-specific strategies

Compute:

• Use committed use discounts / savings plans for baseline workloads
• Right-size VM types before committing (don't lock in oversized instances)
• Shut down non-production environments outside business hours

Storage:

• Use appropriate storage tiers (standard vs SSD) based on IOPS needs
• Set lifecycle policies on object storage to transition old data to cold tiers
• Delete unused snapshots and old backups

Networking:

• Minimize cross-region data transfer
• Use internal load balancers where public access is not needed
• Consider NAT Gateway costs for outbound traffic (batch requests where possible)

General rules

• Start with the biggest cost items first (Pareto principle)
• Measure before optimizing; use cost monitoring dashboards as baseline
• Implement changes incrementally and monitor for performance regression
• Review costs monthly and set budget alerts at 80% and 100% thresholds
• Document cost-saving decisions so they are not reverted unknowingly