name: clone-deployment
description: Production deployment -- Docker, CI/CD, domain, monitoring, launch checklist
category: clone-pipeline
trigger: "deploy clone", "production deployment", "launch clone", "clone deployment"
Clone Deployment
When to Use
- Testing complete, quality verdict is PASS (output of clone-testing)
- Ready to ship to production
- Need repeatable, automated deployment pipeline
Process
- Containerization:
- Dockerfile per service (frontend, backend, worker)
- docker-compose.yml for local orchestration
- Multi-stage builds for minimal image size
- Health check endpoints in each service
- CI/CD pipeline (GitHub Actions):
- On push: lint --> type-check --> unit tests --> build
- On PR merge to main: full test suite --> build images --> push to registry
- On release tag: deploy to production
- Secrets managed via GitHub Secrets / Vault
- Domain and SSL:
- DNS A/CNAME records (Cloudflare or provider)
- SSL via Let's Encrypt (auto-renewal)
- Reverse proxy (nginx / Caddy)
- Monitoring:
- Uptime monitoring (UptimeRobot, Uptime Kuma)
- Error tracking (Sentry)
- Performance APM (response times, error rates)
- AI cost tracking (tokens used, cost per request)
- Launch checklist:
- Post-launch verification:
- Smoke test critical path on production URL
- Verify monitoring receives data
- Check logs for errors in first 15 minutes
Output
- Deployed application accessible via production URL
- CI/CD pipeline (green builds)
- Monitoring dashboard with alerts
- Deployment runbook for future releases
Example
SlideDeck AI deployed: frontend on Vercel, backend on Railway (Docker), PostgreSQL on Supabase. CI/CD: GitHub Actions with 3 stages, 4 min total. Domain: slidedeck.example.com with Cloudflare DNS + SSL. Monitoring: Sentry for errors, UptimeRobot for uptime, custom dashboard for AI token spend ($0.03/generation).