Definition
Scrape vs search for RAG is the decision rule for building large RAG corpora: scrape when you need full page text from URLs you already know (especially behind-auth or JS-heavy targets), search when you can express the corpus as queries against indexed public sources and let a SERP/Reddit/YouTube/Amazon API return typed JSON.
In Depth
An r/Rag post in May 2026 asked which web scraper to use for ~10M tokens of tech articles, docs, blogs, and PDFs. The honest 2026 answer: the question often has the wrong shape. For tech articles + docs (well indexed, well structured), the cheaper and more reliable approach is search-as-source — Scavio Google SERP queries against the topics you want, return organic + featured snippet + AI Overview as typed JSON, then `extract` the top-N URLs into Markdown. This avoids most of the scraping pain (Cloudflare, layout shifts, headless infra) while still giving you the bytes that go into the embeddings. For PDF educational content, the right shape is still scraping + a PDF parser; for behind-auth or JS-heavy targets, scraping is unavoidable. The cost difference: 10M tokens via search-as-source is typically $20-80 in Scavio + extract credits; via brittle scraping + headless infra it is variable but usually higher and operationally heavier.
Example Usage
RAG corpus build for 'AI agent infrastructure' topic. 200 seed queries via Scavio Google → ~5,000 unique URLs → top-2K via /extract → ~8M tokens of clean Markdown. Total Scavio cost ~$50-90. No scraper maintenance, no headless rendering, typed JSON throughout.
Platforms
Scrape vs Search for RAG is relevant across the following platforms, all accessible through Scavio's unified API: