Pinecone
Managed vector database for AI applications
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Discover top open-source software, updated regularly with real-world adoption signals.
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.
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.
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.
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.
When teams consider LanceDB, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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.
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.
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.
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.
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.
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.
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