Building a Retrieval-Augmented Generation (RAG) pipeline requires feeding raw web data into a vector database. But web data is messy, HTML is bloated, and public endpoints aggressively rate-limit incoming traffic. Selecting the right web scraping API to serve as your ingestion layer comes down to three operational factors: native Markdown output for token efficiency, integrated proxy routing for scale, and usage-based pricing to control costs. This guide evaluates the technical requirements for a production-grade scraping infrastructure designed specifically for AI data ingestion. The Shift to AI-Native Data Extraction Traditional web scraping pipelines were built for Extract, Transform, Load (ETL) workflows. Engineers wrote CSS selectors or XPath queries to extract specific fields—like prices, dates, or titles—from HTML tables, dumping the structured data into relational databases for business intelligence. RAG flips this model.…