CAST AI Local Dev Loop

Overview

Fast iteration workflow for CAST AI integrations: test autoscaler policies in a dev cluster, validate Terraform modules before applying to production, and use the CAST AI API to measure savings impact during development.

Prerequisites

• Completed castai-install-auth setup
• A development Kubernetes cluster (kind, minikube, or cloud dev cluster)
• kubectl, helm, and optionally terraform installed

Instructions

Step 1: Project Structure

my-castai-infra/
├── terraform/
│   ├── environments/
│   │   ├── dev.tfvars
│   │   ├── staging.tfvars
│   │   └── prod.tfvars
│   ├── modules/
│   │   └── castai-cluster/
│   │       ├── main.tf
│   │       ├── variables.tf
│   │       └── outputs.tf
│   └── main.tf
├── policies/
│   ├── dev-policy.json
│   └── prod-policy.json
├── scripts/
│   ├── check-savings.sh
│   └── validate-policies.sh
├── .env.dev            # Dev API key (git-ignored)
└── .env.example

Step 2: Dev Cluster with Relaxed Policies

# Connect your dev cluster (read-only first)
helm upgrade --install castai-agent castai-helm/castai-agent \
  -n castai-agent --create-namespace \
  --set apiKey="${CASTAI_API_KEY_DEV}" \
  --set provider="eks"

# Apply development-safe autoscaler policy
curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY_DEV}" \
  -H "Content-Type: application/json" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  -d '{
    "enabled": true,
    "unschedulablePods": { "enabled": true },
    "nodeDownscaler": {
      "enabled": true,
      "emptyNodes": { "enabled": true, "delaySeconds": 300 }
    },
    "clusterLimits": {
      "enabled": true,
      "cpu": { "minCores": 2, "maxCores": 16 }
    }
  }'

Step 3: Quick Savings Check Script

#!/bin/bash
# scripts/check-savings.sh
set -euo pipefail

API_KEY="${CASTAI_API_KEY_DEV}"
CLUSTER_ID="${CASTAI_CLUSTER_ID}"

echo "=== CAST AI Dev Cluster Savings ==="
curl -s -H "X-API-Key: ${API_KEY}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CLUSTER_ID}/savings" \
  | jq '{
    monthlySavings: .monthlySavings,
    percentage: .savingsPercentage,
    spotNodes: [.nodes[] | select(.lifecycle == "spot")] | length,
    totalNodes: [.nodes[]] | length
  }'

Step 4: Terraform Plan-Apply Loop

# Plan with dev variables
cd terraform/
terraform plan -var-file=environments/dev.tfvars -out=plan.tfplan

# Apply and check CAST AI result
terraform apply plan.tfplan

# Verify policies took effect
curl -s -H "X-API-Key: ${CASTAI_API_KEY_DEV}" \
  "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \
  | jq .

Step 5: Watch Node Changes in Real Time

# Terminal 1: Watch CAST AI node operations
watch -n 15 'curl -s -H "X-API-Key: ${CASTAI_API_KEY_DEV}" \
  "https://api.cast.ai/v1/kubernetes/external-clusters/${CASTAI_CLUSTER_ID}/nodes" \
  | jq "[.items[] | {name, instanceType, lifecycle, age: .createdAt}] | length"'

# Terminal 2: Watch kubectl node status
kubectl get nodes -w

Error Handling

Output

The local loop produces a versioned plan, non-production policy result, agent health check, and observed node/policy evidence. Keep development credentials and cluster identifiers isolated from production; a plan or console result is not permission to apply a change to another environment.

Examples

Run terraform plan against a disposable development cluster, inspect the saved plan for destructive changes, then apply only after a reviewer accepts the diff. Confirm policy state and node behavior, and destroy or reset the test resources after observation so temporary optimization settings cannot quietly persist into a later test.

Resources

• CAST AI Terraform Provider
• Autoscaler Policies
• Node Configuration

Next Steps

See castai-sdk-patterns for reusable API wrapper patterns.