Databricks Vector Search vs Pinecone

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

	Databricks Vector Search RAG	Pinecone RAG
Tagline	Managed hybrid vector search that lives inside the Databricks lakehouse and auto-syncs with your source tables.	Managed vector database for production-scale similarity search.
Category	RAG	RAG
Pricing	Enterprise· Consumption-based via Databricks; free trial available	Freemium· Free starter; serverless pay-as-you-go from $0.33/1M reads
Model	Multi-model (BYO embeddings or Databricks-hosted)	Hosted vector DB (not an LLM)
Editorial score	—	8.8 / 10
Use cases	rag-retrievalhybrid-searchagent-memoryproduct-searchrecommendations	managed vector DBproduction RAG
Pros	Auto-syncs indexes from Delta tables — no bespoke embedding pipeline Hybrid semantic + BM25 + reranking in a single API Unity Catalog governance and ACLs extend to the index Serverless, scales to billions of vectors and high QPS	Zero ops Low query latency Mature SDKs Serverless pricing is now sensible
Cons	Only economical if you are already on Databricks Enterprise pricing is opaque without a sales conversation Not open source; lock-in to the Databricks platform Overkill for small RAG prototypes	Costs scale with vector count Less flexible than self-hosted
Website	www.databricks.com	www.pinecone.io

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