📖 The AI Tool Bible

LanceDB vs Weaviate

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

 
LanceDB
RAG
Weaviate
RAG
TaglineOpen-source multimodal lakehouse and vector database built for AI training and retrieval at petabyte scale.Open-source vector DB with hybrid search and modules.
CategoryRAGRAG
PricingFreemium· Open-source free; LanceDB Cloud and Enterprise via contact salesFreemium· Free open-source; cloud from $25/mo
Model—Hosted vector DB (not an LLM)
Editorial score—8.4 / 10
Use cases
vector-searchragmultimodal-datasetstraining-pipelinesdata-curationhybrid-search
self-hosted RAGhybrid search
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
  • Hybrid search built in
  • Self-host or cloud
  • Module ecosystem
  • GraphQL + REST APIs
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
  • More ops than Pinecone if self-hosted
  • Smaller community
Websitelancedb.comweaviate.io
Pick LanceDB if
  • ✅ 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
Pick Weaviate if
  • ✅ Hybrid search built in
  • ✅ Self-host or cloud
  • ✅ Module ecosystem
  • ✅ GraphQL + REST APIs
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