What combination of technologies is best to use with FastAPI for an AI/ML API?

The reliable combination pairs FastAPI with Pydantic v2 for validation, an async database layer so nothing blocks the event loop, and a separate worker for anything that takes longer than a request-response cycle. Pydantic v2's validation runs on a Rust-based core, which is a meaningful speed jump over the pure-Python validation in v1.

What validates the data going in and out?

Pydantic v2 handles request and response schemas natively in FastAPI, and the same models can double as settings management for API keys and model configuration. For structured interaction with an LLM specifically, Pydantic AI builds on this to enforce typed outputs from a model call instead of parsing free-text responses by hand.

What handles the database without blocking requests?

SQLAlchemy 2.0's async session, paired with an async driver like asyncpg for PostgreSQL, is the standard pairing with FastAPI. Using the synchronous version of SQLAlchemy under FastAPI blocks the event loop on every query and cancels out most of the concurrency benefit of choosing an async framework in the first place. If the API does retrieval over embeddings, a vector store such as pgvector, Pinecone, or Qdrant sits alongside the relational database rather than replacing it.

What runs the actual inference or long-running jobs?

Anything that takes more than a second or two, batch inference, model fine-tuning triggers, or multi-step agent workflows, belongs in a background worker rather than inside the request handler. Celery with Redis or RabbitMQ as the broker is the established option; Arq is a lighter async-native alternative that fits naturally with an already-async FastAPI app. For orchestrating multi-step LLM calls with state, LangGraph is a common layer on top of the worker logic.

What serves the model itself?

Small models can load directly in the FastAPI process, but that ties model memory usage to API uptime and makes scaling the two independently harder. Heavier models are usually served separately through something like ONNX Runtime, TorchServe, or vLLM, with the FastAPI layer calling that service over HTTP or gRPC instead of hosting the model in-process.

How does it all ship?

Uvicorn behind Gunicorn for multiple worker processes, packaged into a Docker container, is the standard way to run this stack in production, with the model-serving component deployed and scaled as its own container when it is heavy enough to need separate GPU resources.