Graphify vs LlamaIndex

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

	Graphify RAG	LlamaIndex RAG
Tagline	Open-source on-device knowledge graph engine that turns code, docs, papers, meetings and images into a queryable graph.	Data framework for connecting LLMs to your data.
Category	RAG	RAG
Pricing	Free· MIT-licensed, free forever; cloud tier hinted but unpriced (waitlist)	Freemium· Free open-source; LlamaCloud paid
Model	Multi-model	BYO (Claude / GPT / open)
Editorial score	—	8.7 / 10
Use cases	knowledge-graphcode-searchpersonal-memoryresearch-recallmeeting-intelligence	RAGdata ingestionindexing
Pros	MIT-licensed and runs fully on-device — no data leaves your machine Incremental updates: only changed nodes/edges re-process, scales to millions of files Ingests broad input set: code/AST, docs, papers, meetings, browser history, images Explicit graph beats opaque vector retrieval for traceable, multi-hop questions	Focused on retrieval (not general agent stuff) Many ingestion connectors Strong production patterns LlamaCloud for managed ingestion
Cons	Waitlist / early-access — not generally available yet Cloud tier and any paid plan are unpriced and undefined Marketing-heavy site with limited technical depth on indexing/query API On-device builds at corpus scale will demand serious local compute	API surface is large Documentation can be hard to navigate
Website	graphifylabs.ai	www.llamaindex.ai

Pick Graphify if

✅ MIT-licensed and runs fully on-device — no data leaves your machine
✅ Incremental updates: only changed nodes/edges re-process, scales to millions of files
✅ Ingests broad input set: code/AST, docs, papers, meetings, browser history, images
✅ Explicit graph beats opaque vector retrieval for traceable, multi-hop questions

Pick LlamaIndex if