Our default AI product split: TypeScript for React UX and Python for FastAPI, LangChain, and eval jobs. The pairing trades hipster monoliths for speed, hiring pool, and clear boundaries. Monorepo purists lose hiring speed — our split matches where libraries are strongest and lets frontend and backend ship in parallel from week one. Hiring pools widen when job posts list React plus Python — full-stack TypeScript-only ML hiring is harder in India.
Why Python for AI backend
Ecosystem density: LangChain, LangGraph, PyTorch, RAGAS, Pydantic. Async FastAPI handles SSE streaming and concurrent retrieval. FastAPI auto-generates OpenAPI schemas Pydantic models define — drift between docs and behavior is rare. Celery or ARQ workers handle ingestion without blocking API workers on long embedding jobs. uv or poetry lockfiles keep Python reproducibility — Docker images pin versions for production parity.
Data science and backend share one language for notebooks-to-production pipelines. Type hints and Pydantic models enforce contracts at runtime. Notebooks for data exploration should export functions to tested modules — notebook-only logic becomes production debt. Python 3.11+ async performance matters for concurrent retrieval fan-out. FastAPI lifespan hooks initialize vector clients cleanly — avoid connection leaks on worker restart.
Why TypeScript for frontend
React, Next.js, and streaming chat libraries mature in TS. Shared types from OpenAPI codegen reduce frontend-backend drift. Next.js App Router with server components can proxy auth cookies while still calling Python for inference — BFF optional, not mandatory. Streaming chat components in React 19 benefit from concurrent rendering during token updates. Vitest for frontend plus pytest for backend run in parallel CI — fast feedback loops matter.
Design systems and accessibility tooling favor TS ecosystems. Mobile React Native reuses types for cross-platform products. Shared ESLint and Prettier configs across TS packages keep UI consistent — design tokens live in TS for type-safe theming. Storybook documents streaming chat components in isolation — QA reproduces UI bugs faster.
Integration patterns
Generate OpenAPI from FastAPI; consume in frontend with typed clients. BFF layer optional for auth cookie handling. OpenAPI codegen to TypeScript clients catches API contract breaks at compile time. Contract tests in CI verify frontend mocks match backend responses. Turborepo or nx optional for monorepos — single repo with packages/api and packages/web suffices for many MVPs.
Background workers: Celery, ARQ, or cloud tasks for ingestion. See production LLM API guide for backend details. Mobile React Native shares business types via a packages/types workspace — one source of truth for enums and DTOs. Production LLM API guide covers FastAPI route patterns this stack consumes. Environment variable validation with zod on frontend and pydantic-settings on backend catches config drift early.
RAG, agents, eval, FastAPI — ML ecosystem density.
React UX, streaming, typed API clients.
OpenAPI/codegen — parallel frontend and backend work.
What to do next
Start with one measurable outcome — latency, accuracy, adoption, or revenue — and design your stack around that constraint. GenOrbit ships production AI systems across healthcare, astrology, fintech, and SaaS; if you want a second pair of eyes on architecture or delivery, we are happy to talk.
Related: /services/custom-software-development · /blog/production-llm-api-fastapi-openai · /blog/streaming-llm-responses-react