Agent skill

castai-cost-tuning

Maximize Kubernetes cost savings with CAST AI spot strategies and right-sizing. Use when analyzing cloud spend, optimizing spot-to-on-demand ratios, or configuring CAST AI for maximum savings. Trigger with phrases like "cast ai cost", "cast ai savings", "cast ai spot strategy", "reduce kubernetes cost", "cast ai budget".

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/castai-pack/skills/castai-cost-tuning

SKILL.md

CAST AI Cost Tuning

Overview

Maximize Kubernetes cost savings through CAST AI: spot instance strategies, workload right-sizing, cluster hibernation, and savings tracking. Typical savings: 50-70% on cloud compute costs.

Prerequisites

  • CAST AI Phase 2 enabled with full automation
  • Savings report available (requires 24h+ of data)
  • Understanding of workload criticality tiers

Instructions

Step 1: Analyze Current Savings

bash
# Get savings breakdown
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/savings" \
  | jq '{
    currentMonthlyCost: .currentMonthlyCost,
    optimizedMonthlyCost: .optimizedMonthlyCost,
    monthlySavings: .monthlySavings,
    savingsPercentage: .savingsPercentage,
    spotSavings: .spotSavings,
    rightSizingSavings: .rightSizingSavings
  }'

Step 2: Maximize Spot Usage

bash
# Enable aggressive spot with diversity and fallbacks
curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{
    "enabled": true,
    "spotInstances": {
      "enabled": true,
      "clouds": ["aws"],
      "spotDiversityEnabled": true,
      "spotDiversityPriceIncreaseLimitPercent": 20,
      "spotBackups": {
        "enabled": true,
        "spotBackupRestoreRateSeconds": 600
      }
    }
  }'

Spot allocation strategy by workload tier:

Workload Type Spot % Rationale
Batch jobs, CI runners 100% spot Interruptible, restartable
Stateless APIs (behind LB) 80% spot Can handle brief interruptions
Stateful services, databases 0% spot Use on-demand or reserved
ML training 80-100% spot Checkpointing handles interrupts

Step 3: Workload Right-Sizing

bash
# Get resource waste analysis
curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \
  "https://api.cast.ai/v1/workload-autoscaling/clusters/${CASTAI_CLUSTER_ID}/workloads" \
  | jq '[.items[] | select(.estimatedSavingsPercent > 20) | {
    name: .workloadName,
    namespace: .namespace,
    wastedCpu: (.currentCpuRequest - .recommendedCpuRequest),
    wastedMemory: (.currentMemoryRequest - .recommendedMemoryRequest),
    savingsPercent: .estimatedSavingsPercent
  }] | sort_by(-.savingsPercent) | .[0:10]'

Step 4: Cluster Hibernation (Dev/Staging)

bash
# Hibernate non-production clusters during off-hours
# Scales nodes to zero, resume on demand

# Enable hibernation
curl -X POST -H "X-API-Key: ${CASTAI_API_KEY}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/hibernate" \
  -d '{
    "schedule": {
      "enabled": true,
      "hibernateAt": "20:00",
      "wakeUpAt": "08:00",
      "timezone": "America/New_York",
      "weekdaysOnly": true
    }
  }'

Step 5: Cost Tracking Dashboard

typescript
interface CostReport {
  cluster: string;
  period: string;
  currentCost: number;
  optimizedCost: number;
  savings: number;
  spotPercent: number;
}

async function generateMonthlyCostReport(
  clusterIds: string[]
): Promise<CostReport[]> {
  const reports: CostReport[] = [];

  for (const clusterId of clusterIds) {
    const [cluster, savings, nodes] = await Promise.all([
      castaiGet(`/v1/kubernetes/external-clusters/${clusterId}`),
      castaiGet(`/v1/kubernetes/clusters/${clusterId}/savings`),
      castaiGet(`/v1/kubernetes/external-clusters/${clusterId}/nodes`),
    ]);

    const spotNodes = nodes.items.filter(
      (n: { lifecycle: string }) => n.lifecycle === "spot"
    ).length;

    reports.push({
      cluster: cluster.name,
      period: new Date().toISOString().slice(0, 7),
      currentCost: savings.currentMonthlyCost,
      optimizedCost: savings.optimizedMonthlyCost,
      savings: savings.monthlySavings,
      spotPercent:
        nodes.items.length > 0
          ? (spotNodes / nodes.items.length) * 100
          : 0,
    });
  }

  return reports;
}

Cost Optimization Checklist

  • Spot instances enabled with diversity
  • Workload autoscaler right-sizing resources
  • Dev/staging clusters hibernated off-hours
  • Empty node downscaler enabled
  • Instance families include latest generation (cheaper)
  • Reserved/savings plan for baseline on-demand nodes
  • Weekly savings report review

Error Handling

Issue Cause Solution
Savings lower than expected Too many on-demand constraints Relax node template constraints
Spot interruptions too frequent Single instance type Enable spot diversity
Hibernation not triggering Schedule timezone wrong Use IANA timezone format
Right-sizing too aggressive Low headroom Increase memory headroom to 20%

Resources

Next Steps

For architecture patterns, see castai-reference-architecture.

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