LanceDB vs Weaviate

Pick two tools from the same category to compare side-by-side.

	LanceDB Open-source embedded multimodal vector database built on the Lance columnar format.	Weaviate Open-source, cloud-native vector database in Go that stores both objects and vectors.
Category	retrieval	retrieval
Vendor type	hybrid	hybrid
Pricing	freemium	freemium
Strengths	+ Embedded/serverless design needs no separate server to operate + Stores raw multimodal data alongside vectors with built-in versioning + Multiple query modes in one engine: vector, full-text, and SQL + Open source with native SDKs for Python, TypeScript, and Rust	+ Hybrid vector plus BM25 keyword search in a single query API + Built-in vectorizer integrations generate embeddings at ingest time + Multi-tenancy designed for tens of thousands of tenants + Enterprise features including RBAC, SOC 2, and HIPAA compliance options
Tradeoffs	− Embedded model fits single-node/edge use better than large multi-tenant clusters − Requires bringing your own embedding model for vector generation	− HNSW indexes are memory-hungry, driving up infrastructure cost at large scale − Collection/schema and module configuration adds setup complexity versus simpler stores − Expanding product surface (agents, embeddings, memory) increases the learning surface beyond core vector search
Used in stacks	0	0