Next.js Frontend + FastAPI Backend: The Full-Stack AI Blueprint
This is the architecture I reach for when a client needs a complete AI-powered web application. Next.js 15 handles the frontend and server-side rendering. FastAPI handles the AI backend and LLM orchestration. Here’s how I wire them together cleanly.
Why this split makes sense for AI products
JavaScript excels at UI, real-time updates, and SEO. Python excels at AI, data processing, and the entire ML ecosystem. Mixing them in a single monorepo forces compromises on both. A clean split — Next.js for frontend, FastAPI for AI backend — lets each language do what it does best.
API design: REST for CRUD, SSE for streaming
Standard REST endpoints handle CRUD operations. For LLM responses, I use Server-Sent Events (SSE) from FastAPI to stream tokens to the Next.js frontend in real time. This creates the ChatGPT-style streaming UX that clients now expect, without WebSocket complexity.
Authentication across two services
I use Supabase Auth or JWT tokens issued by FastAPI and validated in Next.js middleware. Never duplicate auth logic — the FastAPI backend validates tokens on every protected route. Next.js middleware handles redirects before the page renders.
Deployment: Vercel + AWS in tandem
Next.js deploys to Vercel (zero config, edge functions, automatic previews). FastAPI deploys to AWS ECS with Docker. A single GitHub Actions workflow runs tests and deploys both on merge to main. Environment variables managed in Vercel dashboard and AWS Secrets Manager.
The folder structure I use in every project
Monorepo with /frontend (Next.js) and /backend (FastAPI). Shared /types directory with TypeScript interfaces and Pydantic schemas kept in sync. A /docs folder with architecture decisions. Docker Compose for local development running both services simultaneously with hot reload.
