The Embedded Vector Database for Enterprise AI

LanceDB is an open source embedded vector database you can run directly inside your application or notebook. No clusters, no containers, no separate service to manage. Install the library, point it at a local path, and start building. Under the hood, LanceDB uses the open Lance lakehouse format optimized for random access and evolving schemas, so the embedded database behaves like a real storage engine that persists data and indexes to disk, not an in-memory demo.

You get the convenience of an in-process storage and query engine for development, plus a clear path to the same technology in a managed environment when you are ready to scale.

Why Developers Choose this Open Source Vector Database

For many teams, an open source vector database is the starting point for any new retrieval project. LanceDB is built for that workflow:

• Open source library you can vendor-review and run on your own hardware
• Native clients for Python and TypeScript, tuned for notebook and service use
• LanceDB is different because it’s built on top of an open lakehouse format, Lance, leveraging the Arrow type system, so you can easily use other Arrow-compatible query engines (like DuckDB) to query your data without vendor lock-in.

Because data is stored on disk using a columnar layout, the embedded database can handle datasets larger than memory and supports training, evaluation, and retrieval from the same tables. You keep the flexibility of an open source vector database while knowing there is a supported path to a managed service when you outgrow a single machine. You also have a host of integrations with the larger open source ecosystem that you can leverage for complimentary workloads, all built on top of the same data foundation.

The examples, issues, and roadmap discussions on the LanceDB GitHub repository are fully transparent, giving you insights into the tool’s evolution and direct access to a huge pool of developers who are actively building the next generation of data infrastructure.

From an Open Source Vector Database on GitHub to Production

Most projects start on a laptop and then hit the same questions: how do we share this with the team, and how do we run it in production?

With LanceDB:

• You can browse code samples and discussions on LanceDB Github and engage with an active community on Discord
• The embedded library and the managed deployment share the same data format and query engine, so your ingestion and update pipelines don’t need to change as you scale your workloads
• Moving from “vector database open source local deployment” to a production deployment on the cloud is just a couple of lines of code changed (simply point to the remote cluster of your deployment)

You keep the quick feedback loop of local development while knowing the endpoint can be swapped for a managed environment when you need autoscaling, uptime guarantees, and larger datasets.

Build Your Prototype Today

Teams often start with LanceDB embedded on a laptop or single VM, then reuse the same tables and queries as datasets grow into multi-terabyte lakes.

• Start with the local library for quick experiments
• Use the managed deployment when you need collaboration, dashboards, and persistent testing environments
• Rely on LanceDB GitHub and an active Discord community for examples and support from the LanceDB maintainers, as well as open source users

Use the form to see how teams move from local notebooks to production retrieval on the same engine.