📖 The AI Tool Bible

DeepSearcher vs LlamaIndex

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

 
DeepSearcher
RAG
LlamaIndex
RAG
TaglineOpen-source agentic RAG framework for private enterprise data, built by the Zilliz/Milvus team.Data framework for connecting LLMs to your data.
CategoryRAGRAG
PricingFree· Free, Apache 2.0; bring your own LLM and vector DB costsFreemium· Free open-source; LlamaCloud paid
ModelMulti-model (DeepSeek, OpenAI o1/o3-mini, Claude, Llama, others)BYO (Claude / GPT / open)
Editorial score—8.7 / 10
Use cases
enterprise-ragagentic-searchprivate-document-qaresearch-agentsknowledge-base-search
RAGdata ingestionindexing
Pros
  • Apache 2.0, fully self-hostable for private data
  • Agentic multi-step retrieval, not just one-shot RAG
  • Pluggable LLMs and vector stores including Milvus
  • Backed by Zilliz, the team behind Milvus
  • Focused on retrieval (not general agent stuff)
  • Many ingestion connectors
  • Strong production patterns
  • LlamaCloud for managed ingestion
Cons
  • Library/CLI, no hosted product or managed API
  • Web crawling and some loaders still in development
  • Requires engineering effort to deploy and tune
  • Best experience assumes you already run Milvus/Zilliz
  • API surface is large
  • Documentation can be hard to navigate
Websitezilliztech.github.iowww.llamaindex.ai
Pick DeepSearcher if
  • ✅ Apache 2.0, fully self-hostable for private data
  • ✅ Agentic multi-step retrieval, not just one-shot RAG
  • ✅ Pluggable LLMs and vector stores including Milvus
  • ✅ Backed by Zilliz, the team behind Milvus
Pick LlamaIndex if
  • ✅ Focused on retrieval (not general agent stuff)
  • ✅ Many ingestion connectors
  • ✅ Strong production patterns
  • ✅ LlamaCloud for managed ingestion
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