> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LanceDB Embedded

> The open-source, embedded retrieval library that runs inside your application process.

**LanceDB Embedded** is an open-source, embedded retrieval library, with client SDKs in Python, TypeScript and
Rust -- the application itself plans queries, builds indexes, and runs background work, with no separate database
server to deploy. It reads and writes tables on local disk or in object storage from inside your application
process, on the same [Lance](https://docs.lancedb.com/basics/lance.md) format and table model that [LanceDB Enterprise](https://docs.lancedb.com/enterprise/index.md)
builds on.

![LanceDB Embedded architecture](https://mintcdn.com/lancedb-bcbb4faf/j6_OQyC0TylLjP5i/static/assets/images/overview/lancedb-embedded.png?fit=max\&auto=format\&n=j6_OQyC0TylLjP5i\&q=85\&s=d2760036dec2a444afd5137454d2100c)

LanceDB Embedded: the LanceDB SDK and the Lance format run in your application, reading and writing tables on local disk or in object storage.

## Why use LanceDB Embedded?

LanceDB Embedded fits local retrieval applications, notebooks, development, and single-node data pipelines, where
your application controls its own compute resources and [table maintenance](https://docs.lancedb.com/maintenance/compaction.md).

* **No infrastructure to run** -- the library runs inside your process. There is no server to deploy, configure,
  or keep available.
* **Free and open source** -- `pip install lancedb` (or the TypeScript or Rust equivalent) and start reading and
  writing tables, with no account or service to set up first.
* **Full local control** -- your application decides where table data lives, when maintenance such as compaction
  and indexing runs, and how much compute each operation gets.
* **The same table format as Enterprise** -- Embedded and [Enterprise](https://docs.lancedb.com/enterprise/index.md) share the same Lance
  format and table model, so moving to a distributed deployment later does not require a data conversion step.

## Get started

> [card] [Quickstart](https://docs.lancedb.com/quickstart.md)
>
> Create a table, add data, and run your first search in a few lines of code.

> [card] [Choose an SDK](https://docs.lancedb.com/basics/sdk.md)
>
> Pick between the Python, TypeScript, and Rust SDKs.

When your data or traffic outgrows a single machine, or you need managed infrastructure, private deployment, or
platform support, see [LanceDB Enterprise](https://docs.lancedb.com/enterprise/index.md) and the
[feature-by-feature comparison](https://docs.lancedb.com/basics/embedded-vs-enterprise.md).


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