Kubernetes Cost Optimizer

Overview

This skill audits Kubernetes clusters for resource inefficiency by comparing requested CPU/memory against actual usage from metrics-server. It identifies zombie deployments, overprovisioned workloads, and generates kustomize-compatible patches for right-sizing with safety buffers.

Instructions

Step 1: Verify Cluster Access and Metrics Availability

Run kubectl cluster-info and kubectl top nodes to confirm connectivity and that metrics-server is running. If metrics-server is unavailable, inform the user and suggest installing it first.

Step 2: Collect Resource Data

For each namespace (or user-specified namespaces):

# Get resource requests/limits for all pods
kubectl get pods --all-namespaces -o json | jq '[.items[] | {
  namespace: .metadata.namespace,
  pod: .metadata.name,
  containers: [.spec.containers[] | {
    name: .name,
    cpu_request: .resources.requests.cpu,
    cpu_limit: .resources.limits.cpu,
    mem_request: .resources.requests.memory,
    mem_limit: .resources.limits.memory
  }]
}]'

# Get actual usage
kubectl top pods --all-namespaces --no-headers

Step 3: Calculate Efficiency Ratios

For each deployment, compute:

• Efficiency = actual_usage / requested × 100
• Waste = 100 - efficiency
• Monthly cost estimate = (requested_cpu × node_cost_per_cpu) + (requested_mem × node_cost_per_gi)

Use these reference prices if the user doesn't specify:

• On-demand: ~$0.0425/vCPU-hour, ~$0.0057/GiB-hour (AWS us-east-1 m5 family)
• Convert to monthly: multiply hourly by 730

Step 4: Identify Zombie Workloads

Flag deployments where:

• CPU usage is < 1m (1 millicore) for 7+ days
• Pod restart count is 0 and replicas are running
• Namespace matches staging/dev/preview patterns

Step 5: Generate Right-Sizing Recommendations

For each overprovisioned workload:

1. Take the p99 usage over available history (ideally 14 days)
2. Add a configurable headroom buffer (default: 30%)
3. Set requests = p99 + buffer, limits = requests × 1.5
4. Output as kustomize patches or plain YAML diffs

Step 6: Produce Summary Report

Format a table showing:

• Top waste offenders sorted by estimated monthly savings
• Total cluster waste percentage
• Projected annual savings
• Risk assessment (low/medium/high) for each recommendation

Examples

Example 1: Namespace Audit

User prompt:

Audit the api-services namespace for resource waste

Agent output:

Resource Audit: api-services namespace
───────────────────────────────────────

Deployment          Replicas  CPU Req→Used   Mem Req→Used    Efficiency  Est. Waste
user-auth           3         500m→120m      1Gi→340Mi       26%         $85/mo
order-processor     2         1000m→650m     2Gi→1.4Gi       68%         $31/mo
email-worker        2         250m→15m       512Mi→80Mi      8%          $42/mo

Recommendations:
1. email-worker: Reduce to 1 replica, set requests to cpu:50m/mem:150Mi → saves $42/mo
2. user-auth: Set requests to cpu:180m/mem:500Mi → saves $68/mo
3. order-processor: Requests are reasonable, minor trim to cpu:850m → saves $12/mo

Total potential savings: $122/month ($1,464/year)

Example 2: Zombie Detection

User prompt:

Find unused deployments across all namespaces

Agent output:

Zombie Workloads Detected
─────────────────────────

Namespace: staging
  ✗ feature-auth-v2      2 replicas  CPU: 0m  Last deploy: 2025-09-14  → $28/mo wasted
  ✗ hotfix-payment-flow   1 replica   CPU: 0m  Last deploy: 2025-10-02  → $14/mo wasted
  ✗ demo-dashboard        3 replicas  CPU: 0m  Last deploy: 2025-07-28  → $42/mo wasted

Namespace: dev
  ✗ test-migration        1 replica   CPU: 0m  Last deploy: 2025-11-18  → $14/mo wasted

Suggested cleanup:
  kubectl delete deployment feature-auth-v2 hotfix-payment-flow demo-dashboard -n staging
  kubectl delete deployment test-migration -n dev

Total zombie cost: $98/month

Guidelines

• Never auto-apply changes — always present recommendations for human review
• Safety buffer is critical — default 30% headroom prevents OOMKills after right-sizing
• Prioritize by savings — show the biggest wins first so users focus effort where it matters
• Account for traffic patterns — warn if usage data covers less than 7 days or misses peak periods
• Consider HPA — if a deployment has a HorizontalPodAutoscaler, note that right-sizing requests affects scaling thresholds
• Staging vs production — be more aggressive with staging recommendations, more conservative with production
• Cost estimates are approximate — note the instance type assumptions and suggest the user verify with their actual pricing