Kubernetes Cost Optimization

Cost analysis and optimization using kubectl-mcp-server's cost tools.

Quick Cost Analysis

Get Cost Summary

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get_namespace_cost(namespace)  # Namespace cost breakdown
get_cluster_cost()             # Cluster-wide cost summary

Find Unused Resources

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find_unused_resources(namespace)  # PVCs, ConfigMaps, Secrets
find_orphaned_pvcs(namespace)     # Unbound PVCs

Resource Right-Sizing

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get_resource_recommendations(namespace)  # CPU/memory suggestions
get_pod_metrics(name, namespace)         # Current usage

Cost Optimization Workflow

1. Identify Overprovisioned Resources

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# Get recommendations
get_resource_recommendations(namespace="production")

# Compare requests vs actual usage
get_pod_metrics(name, namespace)
get_resource_usage(namespace)

2. Find Idle Resources

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# Unused PVCs (not mounted)
find_orphaned_pvcs(namespace)

# ConfigMaps/Secrets not referenced
find_unused_resources(namespace)

3. Analyze Node Utilization

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get_nodes()
get_node_metrics()  # If metrics-server installed

Right-Sizing Guidelines

Current State	Recommendation
CPU usage < 10% of request	Reduce request by 50%
CPU usage > 80% of request	Increase request by 25%
Memory < 50% of request	Reduce request
Memory near limit	Increase limit, monitor OOM

Cost by Resource Type

Compute (Pods/Deployments)

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get_resource_usage(namespace)
get_pod_metrics(name, namespace)

Storage (PVCs)

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get_pvc(namespace)
find_orphaned_pvcs(namespace)

Network (LoadBalancers)

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get_services(namespace)  # Filter type=LoadBalancer
# Each LB has fixed cloud cost

Multi-Cluster Cost Analysis

Compare costs across clusters:

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get_cluster_cost(context="production")
get_cluster_cost(context="staging")
get_cluster_cost(context="development")

Cost Reduction Actions

Immediate Wins

•Delete unused PVCs: find_orphaned_pvcs() then delete
•Right-size pods: Apply get_resource_recommendations()
•Scale down dev/staging: Off-hours scaling

Medium-term Optimizations

•Use Spot/Preemptible nodes: For fault-tolerant workloads
•Implement HPA: Auto-scale based on demand
•Use KEDA: Scale to zero for event-driven workloads

Long-term Strategy

•Reserved instances: For stable production workloads
•Multi-tenant clusters: Consolidate small clusters
•Right-size node pools: Match workload requirements

Automated Analysis Script

For comprehensive cost analysis, see scripts/find-overprovisioned.py.

KEDA for Cost Savings

Scale to zero with KEDA:

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keda_scaledobjects_list_tool(namespace)
keda_scaledobject_get_tool(name, namespace)

KEDA reduces costs by:

•Scaling pods to 0 when idle
•Event-driven scaling (queue depth, etc.)
•Cron-based scaling for predictable patterns

Related Skills

•k8s-autoscaling - HPA, VPA, KEDA
•k8s-troubleshoot - Resource debugging