LanceDB

What it does

LanceDB takes in multimodal data such as images, video, audio, text, annotations, metadata, and embeddings, stored together in one schema-enforced table. It produces versioned datasets that can be curated, extended with derived features, searched with vector, full-text, hybrid, and SQL queries, and read in shuffled, projected batches for model training. The open-source embedded library runs inside your application process against local disk or object storage.

Teams use one table across the data loop: deduplicating and curating training data, adding feature columns without rewriting existing data, branching or rolling back versions, and serving retrieval for search, RAG, or agent memory. The Lance columnar format underneath is open source, and the OSS library and LanceDB Enterprise share the same table model, so work can move from local development to a distributed deployment.

LanceDB suits ML and data engineering teams building training pipelines or retrieval systems on multimodal data. The OSS library is available through Python, TypeScript, and Rust SDKs and a REST API, with a quickstart for local setup. LanceDB Enterprise is set up by contacting the company directly.

What it can do

• Store images, video, audio, text, annotations, embeddings, and features together in one schema-enforced table
• Vector similarity, full-text, and hybrid search with SQL filtering against one table
• Automatic versioning with branch, roll back, and tag without duplicating data
• Add columns and update embeddings without rewriting the table
• Random-access and shuffled reads for GPU-ready training batches
• Distributed search, indexing, and maintenance in LanceDB Enterprise

Works with

• LangChain
• LlamaIndex
• Apache Arrow
• Pandas
• Polars
• DuckDB

Example workflows

Curate and version a training dataset

Input: raw images, annotations, and embeddings in one table. Steps: deduplicate rows, add derived feature columns without rewriting data, tag the version, and branch for an experiment. Output: a tagged, versioned dataset ready for training reads.

Hybrid retrieval for an agent

Input: a table of documents with embeddings and metadata. Steps: run vector and full-text search combined with SQL filters against the same table. Output: ranked results that can feed retrieval-augmented generation or agent memory.

Before you choose

• LanceDB Enterprise pricing and onboarding require contacting the company.
• Performance figures on the homepage are company-reported.

Listed under

• Data
• AI data
• Data curation
• Feature engineering
• Model training
• Multimodal lakehouse
• Open source

Get started

Enterprise setup requires contacting the company.

1. Install the LanceDB OSS library for your language from the quickstart at https://docs.lancedb.com/quickstart.
2. Create a table, define the schema, and load multimodal records using Basic Table Operations.
3. For Enterprise, contact the company to arrange setup and connect using db:// connections.

The OSS library is open source; Enterprise pricing and terms are provided on contact.

We couldn’t find published prices for LanceDB. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• LanceDB | Multimodal Lakehouse for AI ↗www.lancedb.com
• GitHub - lancedb/lancedb: Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less. · GitHub ↗github.com
• LanceDB - LanceDB ↗docs.lancedb.com

Company website

lancedb.com: product, pricing and policy pages

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Social accounts

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Human review

Checked by a person

Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.