LanceDB vs Milvus

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.	Milvus Open-source, cloud-native vector database for large-scale ANN search, with Zilliz Cloud as its managed offering.
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	+ Scales to billions of vectors via a disaggregated, distributed architecture + Three deployment modes span prototyping (Lite) to enterprise clusters + Broad index-type support and hybrid/multi-vector search + Neutral governance under LF AI & Data with a large contributor base
Tradeoffs	− Embedded model fits single-node/edge use better than large multi-tenant clusters − Requires bringing your own embedding model for vector generation	− Distributed mode depends on multiple external components (etcd, message queue, object storage), making self-hosted operations complex − Heavyweight for small or single-node workloads where simpler stores suffice − Feature differences between Lite, Standalone, and Distributed modes can complicate migration paths
Used in stacks	0	0