📖 The AI Tool Bible

Exa vs LlamaIndex

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

 
Exa
RAG
LlamaIndex
RAG
TaglineWeb search API built for AI agents, with structured outputs and token-efficient highlights.Data framework for connecting LLMs to your data.
CategoryRAGRAG
PricingFreemium· Free playground; paid usage-based plans; enterprise on requestFreemium· Free open-source; LlamaCloud paid
ModelProprietary neural + keyword searchBYO (Claude / GPT / open)
Editorial score—8.7 / 10
Use cases
agent-web-searchrag-retrievalcompany-researchpeople-searchcode-searchdeep-research
RAGdata ingestionindexing
Pros
  • Purpose-built for LLM/agent use, not retrofitted consumer search
  • Highlights mode dramatically cuts tokens sent to the model
  • Structured JSON outputs against custom schemas
  • Vertical indexes for companies, people, and code
  • SOC 2 Type II with zero-retention enterprise option
  • Focused on retrieval (not general agent stuff)
  • Many ingestion connectors
  • Strong production patterns
  • LlamaCloud for managed ingestion
Cons
  • Proprietary, closed source
  • Pricing not transparent on homepage beyond the free playground
  • Coverage and freshness depend on Exa's crawl, not yours
  • API surface is large
  • Documentation can be hard to navigate
Websiteexa.aiwww.llamaindex.ai
Pick Exa if
  • ✅ Purpose-built for LLM/agent use, not retrofitted consumer search
  • ✅ Highlights mode dramatically cuts tokens sent to the model
  • ✅ Structured JSON outputs against custom schemas
  • ✅ Vertical indexes for companies, people, and code
Pick LlamaIndex if
  • ✅ Focused on retrieval (not general agent stuff)
  • ✅ Many ingestion connectors
  • ✅ Strong production patterns
  • ✅ LlamaCloud for managed ingestion
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