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71 lines
3.3 KiB
Plaintext
---
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title: "LanceDB OSS or Enterprise?"
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sidebarTitle: "OSS and Enterprise"
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description: "Start with an embedded library, or use the LanceDB Enterprise multimodal lakehouse for shared AI workloads."
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icon: compass
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keywords: ["oss", "enterprise", "embedded", "deployment", "availability", "comparison"]
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---
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LanceDB gives you two ways to work with multimodal data, both built on the open
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[Lance](/basics/lance) format.
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**LanceDB OSS** is an embedded library. Run it inside your application
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with tables on local disk or in object storage. You control the application,
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its resources, and table maintenance, without a separate database service.
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[Try the quickstart](/quickstart) to create a table and run your first search.
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**LanceDB Enterprise** is a multimodal lakehouse for data curation, feature
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engineering, search, and training data access. It adds query-serving capacity,
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data caching, and background operations for teams working with shared
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multimodal datasets.
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[Connect to Enterprise](/basics/connect#enterprise) when your cluster is ready.
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<span id="labels" />
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<span id="availability" />
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## Why choose Enterprise? {#why-enterprise}
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### Serve growing workloads {#serving}
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Add query-serving capacity as more applications and users depend on your data.
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Enterprise spreads query traffic across a fleet and caches frequently read
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table data near compute, reducing repeated reads from object storage. Query
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serving and background work use separate resources, so you can size each for
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your workload. See [Enterprise architecture](/enterprise/architecture).
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### Reduce operational work {#operations}
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Let the service handle routine table maintenance instead of scheduling it in
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application code. Enterprise handles background [compaction](/maintenance/compaction) and can
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[clean up old table versions](/maintenance/cleanup) according to your retention
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policy. Configure these maintenance tasks for your Enterprise cluster so each
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application does not have to schedule them.
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### Fit your deployment requirements {#deployment}
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Choose a managed deployment or bring LanceDB into your own cloud account.
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Enterprise integrates with OpenTelemetry for metrics and traces, helping your
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team monitor the service alongside your applications. Talk to us about your
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cloud, networking, and operational requirements to plan the right deployment.
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The [Enterprise overview](/enterprise) explains the available options.
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### Connect your data workflows {#workflows}
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Use the same tables for retrieval, dataset preparation, and training data access.
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With its [SQL endpoint](/basics/sql) enabled, Enterprise supports analytical
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[curation](/curation), including joins, grouping, and sampling.
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[Feature engineering](/feature-engineering) adds derived columns and tables;
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computed columns and materialized views require the corresponding features to
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be enabled for your Enterprise cluster.
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[Geneva](/geneva) runs Python feature backfills as distributed jobs.
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[Training data loaders](/training/data-loading), available in both offerings,
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read the resulting datasets for model training.
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## Plan your Enterprise deployment {#get-started}
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[Talk to us about your workload](https://www.lancedb.com/contact) to discuss your
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data, query traffic, pipelines, and deployment requirements.
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For a closer look at how the service runs, read the
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[Enterprise architecture](/enterprise/architecture).
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