FastAPI vs Flask for AI Inference APIs

You are building an HTTP API that wraps a GPU-hosted model. FastAPI and Flask are the two most common Python frameworks for this job, and they differ in ways that directly affect inference throughput, streaming capability, and concurrent request handling. This guide breaks down both options for AI inference on dedicated GPU servers.

Feature Comparison

FastAPI for AI Inference

FastAPI’s async architecture makes it the natural choice for inference APIs. While the GPU processes one request, the event loop handles incoming connections without blocking. This matters when your model takes 500ms to 5s per request and you have multiple clients waiting.

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from pydantic import BaseModel
import httpx

app = FastAPI()

class InferenceRequest(BaseModel):
    prompt: str
    max_tokens: int = 256
    temperature: float = 0.7

@app.post("/v1/generate")
async def generate(req: InferenceRequest):
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "http://localhost:8000/v1/completions",
            json={"prompt": req.prompt, "max_tokens": req.max_tokens,
                  "temperature": req.temperature, "stream": False}
        )
    return response.json()

@app.post("/v1/generate/stream")
async def generate_stream(req: InferenceRequest):
    async def event_stream():
        async with httpx.AsyncClient() as client:
            async with client.stream("POST", "http://localhost:8000/v1/completions",
                json={"prompt": req.prompt, "max_tokens": req.max_tokens,
                      "temperature": req.temperature, "stream": True}) as resp:
                async for chunk in resp.aiter_text():
                    yield chunk
    return StreamingResponse(event_stream(), media_type="text/event-stream")

This pattern proxies requests to a vLLM backend while adding validation, rate limiting, and authentication at the FastAPI layer. See the complete FastAPI server build for a production-ready version.

Flask for AI Inference

Flask is simpler and has a larger ecosystem of extensions. For straightforward inference APIs that handle one request at a time or use a worker pool, Flask gets the job done with less boilerplate.

from flask import Flask, request, jsonify, Response
import requests

app = Flask(__name__)

@app.route("/v1/generate", methods=["POST"])
def generate():
    data = request.get_json()
    response = requests.post(
        "http://localhost:8000/v1/completions",
        json={"prompt": data["prompt"], "max_tokens": data.get("max_tokens", 256),
              "temperature": data.get("temperature", 0.7), "stream": False}
    )
    return jsonify(response.json())

@app.route("/v1/generate/stream", methods=["POST"])
def generate_stream():
    data = request.get_json()
    def event_stream():
        with requests.post("http://localhost:8000/v1/completions",
            json={"prompt": data["prompt"], "max_tokens": data.get("max_tokens", 256),
                  "temperature": data.get("temperature", 0.7), "stream": True},
            stream=True) as resp:
            for chunk in resp.iter_content(chunk_size=None):
                yield chunk
    return Response(event_stream(), mimetype="text/event-stream")

Run Flask with Gunicorn and multiple workers to handle concurrency. For a detailed Flask wrapper, see the Flask AI API guide.

Performance Under Load

The critical difference appears under concurrent load. FastAPI’s async event loop handles 50 simultaneous connections with a single process. Flask blocks on each request unless you configure Gevent or run multiple Gunicorn workers, each consuming additional memory.

For LLM inference, the GPU is the bottleneck — not the framework. But FastAPI’s ability to queue and manage waiting requests without spawning threads means lower memory overhead and more predictable latency. This advantage compounds when running WebSocket connections for real-time streaming.

Deployment on GPU Servers

# FastAPI with Uvicorn
pip install fastapi uvicorn httpx
uvicorn app:app --host 0.0.0.0 --port 8080 --workers 1

# Flask with Gunicorn
pip install flask gunicorn requests
gunicorn -w 4 -b 0.0.0.0:8080 app:app

Place either behind Nginx for TLS termination and load balancing. For monitoring, add Prometheus metrics to track request latency and GPU utilisation alongside your self-hosted model.

Which to Choose

Choose FastAPI for new AI inference APIs. Async support, automatic OpenAPI docs, streaming responses, and WebSocket handling make it the better fit for GPU-backed services. The tutorials section has end-to-end FastAPI deployment examples.

Choose Flask when you have existing Flask infrastructure, your team knows Flask well, or your API is simple enough that async provides no benefit. Flask with Gunicorn behind an API gateway can serve production inference workloads reliably. Both frameworks pair well with vLLM and Ollama backends.