---
title: LanceDB
sidebarTitle: "LanceDB"
description: "Multimodal lakehouse for AI."
icon: "/static/assets/logo/lancedb-icon-gray.svg"
keywords: ["multimodal lakehouse", "training", "feature engineering", "search", "open source", "oss"]
---

**LanceDB** is a [multimodal lakehouse](https://lancedb.com/blog/multimodal-lakehouse/) for AI teams that need
one data layer for curation, feature engineering, search and retrieval, and model training.
It is built on top of [Lance](/basics/lance), an open-source lakehouse format designed for multimodal AI data.

Move from data exploration to model training on one, unified platform without needing to manage a
fragmented stack of storage, feature, retrieval, and training systems.

## Build better models, faster {#build-better-models-faster}

Training data and experimentation slow down when raw data, metadata, embeddings, features, and governance
artifacts live in separate systems. LanceDB keeps them together in one versioned multimodal table, so AI teams spend less
time stitching infrastructure together and more time improving datasets, testing features, and keeping GPUs fed.

![Training data lifecycle: Curation, Feature Engineering, Search and Retrieval, Training](/static/assets/images/overview/training-data-lifecycle.svg)

Use the same table to curate training data, add derived features, retrieve examples, and feed training jobs that rely on expensive GPUs.
Training workloads can sample, shuffle, and scan projected columns from local storage or object storage, then assemble
GPU-ready batches from a tagged dataset version.

For a deeper look at how this works in training pipelines, start with [Why LanceDB for training](/training/why-lancedb).

## LanceDB suite {#lancedb-suite}

The LanceDB suite includes LanceDB OSS, an open-source embedded retrieval library, and LanceDB Enterprise,
a multimodal lakehouse platform for the full AI data lifecycle.
OSS is easy to set up on a local machine for search and regular-scale workflows. LanceDB Enterprise is built
for teams that need scale without building bespoke infrastructure for curation,
feature engineering, search and retrieval, and efficient training data access.

![LanceDB suite: OSS search and Enterprise multimodal lakehouse on Lance format](/static/assets/images/overview/lancedb-suite.svg)

## Why teams use LanceDB {#why-teams-use-lancedb}

<Steps>
  <Step title="One table for the whole AI data loop">
    Store images, video, audio, text, annotations, embeddings, and model-generated features together in one schema-enforced table.
    The same table can support dataset curation, feature backfills, experiment splits, retrieval, and training.
  </Step>
  <Step title="High-throughput data access for training">
    Training workloads mix fast random access with high-throughput sequential scans. LanceDB is designed for both, so
    teams can shuffle data into GPU-ready batches more efficiently, improve input throughput, and iterate on experiments faster.
  </Step>
  <Step title="Fast, versatile search and retrieval">
    Whether the end user is a human or an agent, LanceDB powers production retrieval workloads such as semantic search,
    hybrid search, RAG, agent memory, and recommendation systems. Retrieval runs against the same LanceDB tables used
    for curation, feature engineering, and training workflows.
  </Step>
</Steps>

## Start with your workload {#start-with-your-workload}

<CardGroup cols={2}>
  <Card title="Train and fine-tune models" icon="fire" href="/training/why-lancedb">
    Learn why LanceDB works well as the data layer for training workloads.
  </Card>
  <Card title="Load data into PyTorch" icon="boxes-stacked" href="/training/data-loading">
    Use LanceDB tables and permutations for projected, shuffled, random-access training reads.
  </Card>
  <Card title="Browse ready-to-use datasets" icon="database" href="/datasets">
    Explore Lance-formatted multimodal datasets with raw bytes, metadata, embeddings, and indices.
  </Card>
  <Card title="Build search and retrieval" icon="search" href="/search/">
    Use vector search, full-text search, hybrid search, reranking, filtering, and SQL.
  </Card>
</CardGroup>

## From local development to production scale {#from-local-development-to-production-scale}

LanceDB OSS and LanceDB Enterprise share the same Lance format and table model. Start locally with the embedded OSS
library, then move to Enterprise when your team needs distributed scale, managed infrastructure, private deployment,
or higher-throughput curation, feature engineering, search and retrieval, and training workflows.

### 1\. LanceDB OSS {#1-lancedb-oss}
The fastest way to get started is the open-source embedded library, with client SDKs in Python, TypeScript
and Rust. Run it locally in just a few steps, which lets you explore datasets, curate data, and run search and retrieval workloads
for agents. Start here:

<Columns cols={2}>
  <Card
    title="Quickstart"
    icon="rocket"
    href="/quickstart"
  >
    Get started with LanceDB in minutes.
  </Card>
  <Card
    title="Basic Table Operations"
    icon="table"
    href="/management/table"
  >
    Create tables, evolve schemas, version data, and modify rows in LanceDB.
  </Card>
</Columns>

### 2\. LanceDB Enterprise {#2-lancedb-enterprise}

[LanceDB Enterprise](/enterprise) is a petabyte-scale (and beyond), distributed **multimodal lakehouse** platform built for
search, curation, feature engineering, and high-throughput training data access workflows on top of the same core table
abstraction. This eliminates the need for teams to build bespoke infrastructure to manage large multimodal datasets.
To set up LanceDB Enterprise in your organization,
[reach out to us](https://www.lancedb.com/contact).

<Info>
**Built with scale, performance, and security in mind.**

LanceDB Enterprise is designed for very large-scale, high-performance, distributed workloads in
private deployments, and can operate under strict [security requirements](/enterprise/security).
</Info>

<Card
  title="Quickstart"
  icon="rocket"
  href="/quickstart"
>
  Get started with LanceDB in minutes, including Enterprise `db://` connections.
</Card>