Graphify vs Pinecone

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

	Graphify RAG	Pinecone RAG
Tagline	Open-source on-device knowledge graph engine that turns code, docs, papers, meetings and images into a queryable graph.	Managed vector database for production-scale similarity search.
Category	RAG	RAG
Pricing	Free· MIT-licensed, free forever; cloud tier hinted but unpriced (waitlist)	Freemium· Free starter; serverless pay-as-you-go from $0.33/1M reads
Model	Multi-model	Hosted vector DB (not an LLM)
Editorial score	—	8.8 / 10
Use cases	knowledge-graphcode-searchpersonal-memoryresearch-recallmeeting-intelligence	managed vector DBproduction RAG
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	Zero ops Low query latency Mature SDKs Serverless pricing is now sensible
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	Costs scale with vector count Less flexible than self-hosted
Website	graphifylabs.ai	www.pinecone.io

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 Pinecone if