Pinecone
Managed vector database for AI applications
LanceDB
Multimodal AI lakehouse with fast, scalable vector search
Developer-friendly vector database built on Lance columnar format. Store, index, and search petabytes of multimodal data with vector similarity, full-text search, and SQL support.
Overview
The Multimodal AI Lakehouse
LanceDB is a production-ready vector database designed for AI/ML applications that need to work with multimodal data at scale. Built on the Lance columnar format, it enables developers to store, index, and search petabytes of vectors alongside text, images, videos, point clouds, and other data types.
Fast, Flexible Search
LanceDB delivers millisecond vector search across billions of records using state-of-the-art indexing. Beyond vector similarity, it supports full-text search and SQL queries, giving teams comprehensive search capabilities in a single platform. Zero-copy operations and automatic versioning eliminate infrastructure overhead while GPU acceleration speeds up index building.
Built for Developers
Available as both an embedded database and a managed cloud service, LanceDB runs locally or in your infrastructure with no vendor lock-in. Python, TypeScript, Rust, and REST APIs provide native integration options. The rich ecosystem includes seamless connections to LangChain, LlamaIndex, Apache Arrow, Pandas, Polars, and DuckDB, making it easy to incorporate into existing AI workflows.
Highlights
Search billions of vectors in milliseconds with advanced indexing and GPU support
Unified platform for vector similarity, full-text search, and SQL queries
Store and query multimodal data including text, images, videos, and point clouds
Zero-copy operations with automatic versioning and no extra infrastructure
Pros
- 100% open source with Apache-2.0 license and no vendor lock-in
- Native SDKs for Python, TypeScript, and Rust plus REST API
- Built on efficient Lance columnar format for analytics and storage
- Rich integrations with LangChain, LlamaIndex, Pandas, Polars, and DuckDB
Considerations
- Relatively newer project compared to established vector databases
- GPU support limited to index building operations
- Cloud/enterprise features require separate managed service
- Documentation and ecosystem still evolving for advanced use cases
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Fit guide
Great for
- AI/ML teams building multimodal search and retrieval applications
- Developers needing embedded vector search without external dependencies
- Organizations requiring data sovereignty with local or self-hosted deployment
- Projects integrating vector search with LangChain, LlamaIndex, or analytics tools
Not ideal when
- Teams requiring mature enterprise support and extensive production tooling
- Applications needing real-time GPU-accelerated query execution
- Projects with minimal multimodal or vector search requirements
- Organizations seeking established vendor ecosystems with extensive third-party integrations
How teams use it
Semantic Image Search
Index millions of images with embeddings and enable users to search by visual similarity, keywords, or SQL filters across metadata in milliseconds.
RAG-Powered Chatbots
Build retrieval-augmented generation systems with LangChain or LlamaIndex that search document embeddings and return contextually relevant answers.
Recommendation Systems
Store user and item embeddings alongside behavioral data to deliver personalized recommendations using vector similarity and SQL-based filtering.
Multimodal Analytics
Combine vector search with columnar analytics using DuckDB or Polars to analyze patterns across text, images, and structured data in one platform.
Tech snapshot
Rust43%
Python41%
TypeScript15%
Shell1%
Java1%
JavaScript1%
Frequently asked questions
What makes LanceDB different from other vector databases?
LanceDB is built on the Lance columnar format, enabling efficient storage and analytics alongside vector search. It supports multimodal data natively and offers vector similarity, full-text search, and SQL in one platform with zero-copy operations and automatic versioning.
Can LanceDB run without external infrastructure?
Yes, LanceDB is designed as an embedded database that runs locally or in your own cloud infrastructure. It requires no separate servers or services, though a managed cloud option is available for production-scale deployments.
Which programming languages does LanceDB support?
LanceDB provides native SDKs for Python, TypeScript, and Rust, plus a REST API for other languages. This makes it easy to integrate into diverse application stacks and AI/ML workflows.
How does LanceDB handle versioning and data management?
LanceDB includes automatic versioning built into the Lance format, allowing you to manage data versions without additional infrastructure. This simplifies rollback, auditing, and experimentation workflows.
What integrations are available for AI frameworks?
LanceDB integrates with LangChain, LlamaIndex, Apache Arrow, Pandas, Polars, and DuckDB. These integrations enable seamless incorporation into RAG pipelines, analytics workflows, and data processing tasks.
Project at a glance
Active- Stars
- 8,570
- Watchers
- 8,570
- Forks
- 714
LicenseApache-2.0
Repo age2 years old
Last commit6 hours ago
Self-hostingSupported
Primary languageRust
Last synced 4 hours ago