Pinecone vs RAGs by LlamaIndex

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

	Pinecone RAG	RAGs by LlamaIndex RAG
Tagline	Managed vector database for production-scale similarity search.	Open-source Streamlit app that builds a custom RAG pipeline from a natural-language brief.
Category	RAG	RAG
Pricing	Freemium· Free starter; serverless pay-as-you-go from $0.33/1M reads	Free· Free, MIT-licensed; bring your own model/API keys
Model	Hosted vector DB (not an LLM)	Multi-model (OpenAI, Anthropic, Replicate, HuggingFace)
Editorial score	8.8 / 10	—
Use cases	managed vector DBproduction RAG	natural-language-rag-builderdocument-qallamaindex-prototypingchatbot-over-private-data
Pros	Zero ops Low query latency Mature SDKs Serverless pricing is now sensible	MIT-licensed and self-hostable with full control over data Natural-language interface to configure a real LlamaIndex RAG pipeline Provider-agnostic: OpenAI, Anthropic, Replicate and HuggingFace LLMs Exposes chunk size, top-K and embedding model as tunable knobs
Cons	Costs scale with vector count Less flexible than self-hosted	Streamlit reference app, not a production-grade hosted service Maintenance-mode repo with relatively few commits Requires your own API keys and infra to run No built-in auth, eval or multi-tenant support
Website	www.pinecone.io	github.com

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