Railway for FastAPI backends

When your AI app needs a long-running backend (RAG ingest, custom embedding pipelines), Railway is the cheapest way to host it.

Vercel is a courier for parcels. Railway is a warehouse where the worker actually lives.

Railway runs containerized services with a generous free trial and pay-as-you-grow pricing. Connect a GitHub repo, set env vars, deploy. It runs persistently (no cold starts unless you want them).

Use it for:

• FastAPI backends
• Persistent vector DB hosts
• Background workers (Celery, RQ)
• Scheduled ingest jobs

A minimal FastAPI:

# main.py
from fastapi import FastAPI
from pydantic import BaseModel
from openai import OpenAI
import os

app = FastAPI()
client = OpenAI()

class Q(BaseModel):
    question: str

@app.post("/chat")
def chat(q: Q):
    r = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": q.question}],
    )
    return {"answer": r.choices[0].message.content}

requirements.txt:

fastapi
uvicorn
openai

Procfile (Railway):

web: uvicorn main:app --host 0.0.0.0 --port $PORT

Deploy: connect GitHub repo to Railway, set OPENAI_API_KEY, click deploy.