📖 The AI Tool Bible

Databricks Vector Search vs Weaviate

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

 
Databricks Vector Search
RAG
Weaviate
RAG
TaglineManaged hybrid vector search that lives inside the Databricks lakehouse and auto-syncs with your source tables.Open-source vector DB with hybrid search and modules.
CategoryRAGRAG
PricingEnterprise· Consumption-based via Databricks; free trial availableFreemium· Free open-source; cloud from $25/mo
ModelMulti-model (BYO embeddings or Databricks-hosted)Hosted vector DB (not an LLM)
Editorial score—8.4 / 10
Use cases
rag-retrievalhybrid-searchagent-memoryproduct-searchrecommendations
self-hosted RAGhybrid search
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
  • Hybrid search built in
  • Self-host or cloud
  • Module ecosystem
  • GraphQL + REST APIs
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
  • More ops than Pinecone if self-hosted
  • Smaller community
Websitewww.databricks.comweaviate.io
Pick Databricks Vector Search if
  • ✅ 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
Pick Weaviate if
  • ✅ Hybrid search built in
  • ✅ Self-host or cloud
  • ✅ Module ecosystem
  • ✅ GraphQL + REST APIs
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