The Deployment Decision That Shapes Your Architecture
Docker vs serverless shapes how you write code, scale, and pay. For AI SaaS, this choice matters more because of long-running AI inference workloads that break serverless time limits.
Serverless: Fast to Market
Vercel, AWS Lambda, Cloudflare Workers. Zero ops, auto-scale, pay-per-request. Perfect for AI SaaS MVP using API-based models (OpenAI, Anthropic). Limits: 10–60 second execution max, cold starts on idle.
Docker: Full Control
Needed when: running local AI models (Ollama, vLLM), long batch jobs (>15 min), persistent WebSocket connections, GPU inference, HIPAA compliance. Deploy to Railway, ECS, or Kubernetes.
The Recommended Hybrid
Use both: Vercel for frontend and API routes, Docker on Railway for AI model serving and background jobs, connected via a queue (BullMQ/SQS). This gives you the best of both worlds — fast deploys for product features, stable long-running containers for AI workloads.
Cost at Scale
At 1M requests/month: serverless ~$50, Docker containers ~$200–500. At 10M: serverless ~$500, containers ~$300–600. Containers become cost-effective at high sustained traffic.