Building a Web Scraping SaaS: Architecture, Billing, and Scaling

From Script to Business You have built web scrapers for yourself. Now it is time to turn that skill into a product. A web scraping SaaS lets customers submit URLs and receive structured data — no coding required on their end. This guide covers the architecture, billing, and scaling decisions you will face. Architecture Overview A scraping SaaS has three layers: API Layer — receives scraping requests, authenticates users Worker Layer — executes scrapes, manages browser pools Storage Layer — caches results, stores user data Client -> API Gateway -> Task Queue -> Worker Pool -> Proxy Layer -> Target Site | | Auth/Billing Results DB The API Layer (FastAPI) from fastapi import FastAPI , HTTPException , Depends , Header from pydantic import BaseModel from typing import Optional import uuid import redis import json app = FastAPI ( title = " ScrapeService API " ) redis_client = redis . Redis ( host = " localhost " , port = 6379 , decode_responses = True ) class ScrapeRequest ( BaseModel ): url