castai-cost-tuning

name	castai-cost-tuning
description	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".
allowed-tools	Read, Write, Edit, Bash(curl:*), Grep
version	1.4.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","kubernetes","cost-optimization","castai"]
compatibility	Designed for Claude Code

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

# 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

# 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