Hosting, Serving, and Scaling LLMs in Production
Explore how to transition from scripts to a production-ready LLM service by building an asynchronous FastAPI API, validating inputs with Pydantic, and packaging with Docker. Understand concurrency handling, dependency injection, and deployment best practices to ensure scalable, secure, and reproducible LLM production systems.
We have spent the last few lessons writing Python scripts.
We have scripts to ingest data, search it, and generate answers. But a script is not a server. If we were to wrap our current code in a basic web server (such as a simple Flask app) and deploy it, we would encounter an immediate issue.
LLM operations are I/O bound and slow. Generating an answer takes 5–10 seconds. In a synchronous server, while the LLM is thinking for User A, the entire server freezes. Users B, C, and D are blocked, waiting for User A to finish.
This lesson solves the problem of operationalizing LLM code. We will move from ad-hoc scripts to a production service.
We will build a high-performance FastAPI application designed specifically for long-running, I/O-heavy LLM requests. We will enforce strict input contracts using Pydantic to prevent malformed or abusive requests from ever reaching the model.
Finally, we will package the entire application into a Docker container, ensuring that our service is reproducible, deployable, and safe to run in production.
The serving model for Async, I/O-bound workloads
To handle LLM traffic correctly, we must abandon the traditional synchronous request model and adopt asynchronous concurrency.
FastAPI is built on the Asynchronous Server Gateway Interface (ASGI) standard. Unlike older