An r/Rag post asked what web scraper to use for huge RAG data. The reframe: for a large share of RAG use cases, search APIs replace scrapers entirely. Structured JSON from search beats raw HTML parsing. Five APIs ranked for scrape-free RAG.
Top Pick
Scavio returns typed JSON from 5 platforms — Google, Reddit, YouTube, Amazon, Walmart — giving RAG pipelines diverse, structured source data without any scraping infrastructure.
Full Ranking
#1Our Pick
Scavio
Multi-source RAG from 5 platforms
Pros
- Structured JSON from Google + Reddit + YouTube + Amazon + Walmart
- No scraping infrastructure needed
- Content extraction via /extract endpoint
Cons
- Not a replacement for behind-auth sources
#2
Exa
Semantic RAG with contents included
Pros
- Neural search finds conceptually relevant docs
- Contents included in search price
- Clean text extraction
Cons
- No platform-specific data
- Different from keyword search
#3
Tavily
Simple RAG web search with LangChain
Pros
- LangChain-native RAG tools
- Research API for deep search
- Clean JSON
Cons
- 4K credits at $30 vs 7K for Scavio
- Web only
#4
Firecrawl
Full-page extraction for RAG
Pros
- Purpose-built for web extraction
- Handles JS rendering
- Markdown output
Cons
- Scraping-shaped, not search-shaped
- Anti-bot issues on some sites
#5
Brave Search API
Budget RAG web search
Pros
- Cheapest per-query
- Independent index
Cons
- No contents in base response
- Web only
Side-by-Side Comparison
| Criteria | Scavio | Runner-up | 3rd Place |
|---|---|---|---|
| Structured output | Typed JSON per platform | Clean text (Exa) | JSON (Tavily) |
| Source diversity | 5 platforms | Web (semantic) | Web (keyword) |
| Behind-auth sources | No | No | Limited (Firecrawl) |
| RAG cost (1K docs) | $5 | $7 | $5-30 |
Why Scavio Wins
- For behind-auth sources, JS-heavy SPAs, or proprietary portals, Firecrawl or dedicated scrapers are still needed. Search APIs replace scraping for PUBLIC, INDEXED content only.
- Exa's semantic search is genuinely better for RAG when you need conceptually related documents rather than keyword matches. For research RAG, Exa is a strong choice.
- The r/Rag discussion revealed SearXNG + Crawl4AI failing at scale. The failure mode is upstream IP bans. Search APIs avoid this because they query indexes, not source sites.
- RAG cost math: 1K documents from 200 seed queries via Scavio = $1 in API cost. The equivalent scraping infrastructure (proxies, headless browsers, error handling) costs more in maintenance time alone.
- Multi-source RAG is Scavio's unique advantage: a knowledge base built from Google articles + Reddit discussions + YouTube transcripts is richer than web-only sources.