FastAPI Project Architecture: Layering Routes and Domain Services

Whiteboard: FastAPI — FastAPI Project Architecture.

FastAPI makes it easy to start in one file—and easy to end up with an unmaintainable global script. A durable structure lets routes handle HTTP, services handle use cases, and repositories handle persistence.

Core mental model

A useful direction is router → service → repository/provider, with dependencies injected through function parameters. Domain services should not depend on Request or HTTP status codes, so they can be reused by jobs and tests.

Key mechanics

Load and validate configuration at startup. Create and close pools in the lifespan context. Map exceptions centrally to a stable error schema.

Python example

from fastapi import APIRouter, Depends
from pydantic import BaseModel

router = APIRouter(prefix="/v1/chat", tags=["chat"])

class ChatRequest(BaseModel):
    message: str

class ChatResponse(BaseModel):
    answer: str

@router.post("", response_model=ChatResponse)
async def chat(req: ChatRequest, service = Depends(get_chat_service)):
    return ChatResponse(answer=await service.answer(req.message))

Course focus

This article turns the whiteboard into explicit engineering boundaries: define inputs and outputs first, then decide how state, failures, and observability work. The examples use Python and focus on durable design principles rather than a particular provider version; verify APIs against the documentation for your installed dependencies.

Engineering practice

Keep business rules in the application layer instead of hiding them in untestable prompts or route handlers.
Add timeouts, bounded retries, and idempotency keys to external calls; retries are not a complete error strategy.
Record a request id, latency, input version, model/index version, and outcome without logging sensitive raw content.
Use a small fixed regression set first, then monitor quality and cost with sampled production traffic.

Common mistakes

Drawing only the happy path and omitting timeouts, empty results, rate limits, and rollback paths.
Letting one function parse input, call providers, build prompts, and persist data.
Replacing typed contracts with string conventions that can only be verified by manual integration.

Production checklist

Inputs, outputs, and error responses have explicit schemas
External dependencies have timeouts, bounded retries, rate limits, and fallbacks
Logs, metrics, and traces can be correlated to one request
Critical paths have unit tests, integration tests, and offline evaluation samples
Secrets, user content, and provider responses follow least-privilege and privacy rules

Practice

Implement the smallest loop shown on the whiteboard. Inject a timeout, an empty result, and a malformed payload, then check whether the system remains stable and diagnosable. Add one metric that proves your optimization improved quality or latency.

Hands-on exercise

Split a model-calling route into api, services, providers, and schemas. Inject a fake provider into the service and use TestClient to verify error mapping.