RAG applications need search APIs that return high-quality, relevant results fast enough to not bottleneck the generation step. The best search API for RAG must provide clean structured text for context injection, support fallback from local indices to web search, and price affordably since RAG can trigger multiple searches per user query. We tested five APIs specifically for RAG retrieval quality.
Scavio wins for RAG because it returns structured snippets that inject cleanly into LLM context, supports platform routing for diverse retrieval (Google facts + Reddit opinions + YouTube tutorials), and costs $0.005/query so multi-retrieval RAG stays affordable.
Full Ranking
Scavio
RAG systems needing multi-platform retrieval at low cost
- Structured snippets ready for LLM context injection
- Multi-platform retrieval: Google, Reddit, YouTube
- $0.005/query for affordable multi-retrieval
- AI Overview text provides pre-synthesized context
- Consistent format across platforms
- No vector similarity search
- No document chunking (returns snippets)
Exa
Semantic search for research-heavy RAG
- Neural semantic search
- Good for finding conceptually similar content
- Clean content extraction
- Expensive at RAG scale
- Single search approach
- No platform routing
Tavily
Research-focused RAG with document extraction
- Research API mode returns full document content
- Good LangChain integration
- Designed for RAG use cases
- Acquired by Nebius
- Single platform
- Higher per-query cost
Brave Search API
Non-Google retrieval for RAG diversity
- Independent index adds retrieval diversity
- Simple API
- Fast responses
- No free tier
- Single platform
- Basic snippets only
Serper
Budget Google-only RAG retrieval
- Simple Google snippets
- Fast responses
- Low per-query cost
- Google only
- Basic snippet quality
- No multi-platform
Side-by-Side Comparison
| Criteria | Scavio | Runner-up | 3rd Place |
|---|---|---|---|
| Retrieval Quality | High (structured + AI Overview) | High (semantic) | High (research mode) |
| Platforms | 5 | 1 (Exa index) | 1 (Google) |
| Cost per RAG query (3 searches) | $0.015 | $0.03+ | $0.024 |
| AI Overview Context | Yes | No | No |
| Document Extraction | Via /extract endpoint | Built-in | Built-in (research mode) |
| Free Tier | 500/mo | 1,000 trial | 1,000 trial |
Why Scavio Wins
- Google AI Overview text provides pre-synthesized context that is already optimized for LLM consumption. Injecting this alongside organic snippets gives the RAG system both detailed sources and a summary.
- Multi-platform retrieval means RAG can pull Google facts, Reddit community opinions, and YouTube references into the same context window for richer, more nuanced responses.
- At $0.005/query, a RAG system that runs 3 searches per user query costs $0.015 per interaction. At 1000 queries/day, that is $15/day vs $30+ with Exa or Tavily.
- The /extract endpoint provides full page content when snippets are not sufficient, giving RAG systems a two-tier retrieval strategy: fast snippets first, full extraction for high-value pages.
- Consistent JSON format across platforms means the same context formatting logic works regardless of whether the retrieval came from Google, Reddit, or YouTube.