Open-source embedded vector database built on the Lance columnar format, designed for multimodal AI workloads including RAG, agent memory, semantic search, and recommendation systems.
Open-source vector database that runs embedded in your app — no server needed. Built for RAG, AI agents, and semantic search with support for text, images, video, and more.
LanceDB is an open-source, embedded vector database built on the Lance columnar data format — a format designed specifically for multimodal data and machine learning workloads that benchmarks up to 100x faster than Apache Parquet. LanceDB runs in-process alongside your application with no separate server to manage, making it uniquely simple to deploy for AI-powered search, RAG pipelines, agent memory, and recommendation systems. It supports vector similarity search, full-text search, and SQL queries over the same tables, allowing developers to store vectors, metadata, and multimodal data (text, images, video, point clouds) together and query them through a unified API. LanceDB provides Python, TypeScript, and Rust SDKs, native versioning with zero-copy time-travel queries, and automatic data management. For production workloads, LanceDB Cloud offers a fully managed serverless option with automatic indexing, compaction, and S3-compatible object storage — scaling from prototypes to billions of vectors. The Enterprise tier adds a distributed SQL engine, multimodal data preprocessing, and deployment on any cloud provider.
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Runs in-process alongside your application — no separate database server, no network latency, no ops overhead. Import the library and start querying immediately.
Use Case:
Developers building AI-powered desktop apps, CLI tools, or edge deployments where running a separate database server is impractical
Purpose-built columnar format for multimodal data and ML workloads, delivering up to 100x faster random access than Apache Parquet with native support for nested types and large binary blobs
Use Case:
ML teams storing and querying mixed datasets of embeddings, images, and metadata without format conversion overhead
Combines vector similarity search, BM25 full-text search, and SQL filtering in a single query, enabling sophisticated retrieval strategies without stitching together multiple systems
Use Case:
RAG pipelines that need to combine semantic similarity with keyword matching and metadata filtering for high-precision retrieval
Automatic dataset versioning with zero-copy branching and time-travel queries — inspect or roll back to any previous state without duplicating data
Use Case:
ML experiment tracking where teams need to compare retrieval results across different embedding model versions
LanceDB Cloud provides a fully managed, serverless vector search service with automatic indexing, compaction, and usage-based pricing — no infrastructure management required
Use Case:
Startups scaling from prototype to production without hiring a database operations team
Free
Usage-based (pay as you go)
Custom
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