LanceDB vs Pinecone

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

	LanceDB RAG	Pinecone RAG
Tagline	Open-source multimodal lakehouse and vector database built for AI training and retrieval at petabyte scale.	Managed vector database for production-scale similarity search.
Category	RAG	RAG
Pricing	Freemium· Open-source free; LanceDB Cloud and Enterprise via contact sales	Freemium· Free starter; serverless pay-as-you-go from $0.33/1M reads
Model	—	Hosted vector DB (not an LLM)
Editorial score	—	8.8 / 10
Use cases	vector-searchragmultimodal-datasetstraining-pipelinesdata-curationhybrid-search	managed vector DBproduction RAG
Pros	Open-source Lance format with embedded Python, TS, and Rust libraries Handles vector, full-text, and hybrid search plus SQL filters Scales to 100B+ rows and petabyte multimodal datasets on S3 Git-like versioning, branching, and lineage for training data Used in production by Runway, Character.AI, Netflix, Uber, NVIDIA	Zero ops Low query latency Mature SDKs Serverless pricing is now sensible
Cons	Cloud and Enterprise pricing is not public Broader lakehouse feature set is overkill for simple RAG apps Newer operational tooling than mature databases like Postgres+pgvector	Costs scale with vector count Less flexible than self-hosted
Website	lancedb.com	www.pinecone.io

Pick LanceDB if

Pick Pinecone if