LanceDB
Embedded multimodal vector database on the Lance format
LanceDB is profiled here as a RAG Framework tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
LanceDB is an open-source vector database built on the Lance columnar format, founded in 2022 by Chang She and Lei Xu. It runs in-process like an embedded database and stores data directly on object storage, which separates compute from storage and keeps large multimodal datasets cheap to hold. Versioned tables and zero-copy reads make it a fit for AI workloads that combine retrieval with training data management. The Lance format supports time-travel queries, so a table can roll back to an earlier version for reproducible experiments or audits.
Key Capabilities:
Embedded, serverless operation with no separate database server
Storage on S3, GCS, and Azure through the Lance columnar format
Vector, full-text, and hybrid search with metadata filtering
Multimodal storage for text, images, and embeddings together
Automatic data versioning with zero-copy reads
Apache 2.0 license with Python, TypeScript, and Rust APIs
Alternative tools
- Milvus
Open-source vector database built for billion-scale search
- Sentence Transformers
Python framework for dense text and image embeddings
- R2R
Production retrieval system with ingestion and an API
- Mem0
Long-term memory layer for AI agents and assistants
- Docling
Open-source document conversion built for RAG pipelines
- Unstructured
Turn raw documents into LLM-ready structured data