Pinecone vs RAGFlow

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

	Pinecone RAG	RAGFlow RAG
Tagline	Managed vector database for production-scale similarity search.	Open-source RAG engine with deep document parsing, hybrid search, and visual agent orchestration.
Category	RAG	RAG
Pricing	Freemium· Free starter; serverless pay-as-you-go from $0.33/1M reads	Freemium· Free tier; Starter $29/mo; Pro $129/mo; Enterprise custom
Model	Hosted vector DB (not an LLM)	Multi-model
Editorial score	8.8 / 10	—
Use cases	managed vector DBproduction RAG	document-qaenterprise-searchagent-orchestrationknowledge-basehybrid-retrieval
Pros	Zero ops Low query latency Mature SDKs Serverless pricing is now sensible	Strong deep-document parsing for messy PDFs, tables, and scans Hybrid vector + BM25 retrieval with citation-grounded answers Fully open-source with active GitHub repo and self-host option Visual agent builder plus MCP integration for tool-calling clients Model-agnostic; works with most major LLM providers
Cons	Costs scale with vector count Less flexible than self-hosted	Free tier blocks API access, pushing real use to paid plans Self-hosting is non-trivial and resource-hungry Documentation and UI lag behind the engine's capabilities
Website	www.pinecone.io	ragflow.io

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