--- title: dlt and dltHub description: A map of the dlt and dltHub ecosystem keywords: [dlt, dlthub, open source, source available, pricing, license, workspace, workbench, skills, ai, agent] --- :::note **dltHub offers two products**: dlt (open source) and **dltHub** (commercial). This page explains both products and how they relate. - **dlt** — the open source ingestion library, Apache 2.0. - **dltHub** — the agentic platform that deploys, monitors, and scales dlt pipelines, with a managed runtime, data quality, transformations, and AI tooling for coding agents. [**All dltHub components are available with a commercial license.**](license.md) ::: ## The two products at a glance dlt is free and open source under Apache 2.0, dltHub is a paid product. | Capability | dlt | dltHub | |---|---|---| | Build ingestion pipelines using [dlt verified sources](../dlt-ecosystem/verified-sources) (except premium sources) | ✅ | ✅ | | Build pipelines with the [dltHub AI Workbench](https://github.com/dlt-hub/dlthub-ai-workbench) | — | ✅ | | [Data quality metrics & checks](features/quality/data-quality) | — | ✅ | | Build transformation pipelines (dltHub/dbt) | — | ✅ | | Managed runtime: deploy, run/schedule pipelines, serve data apps, monitor jobs | — | ✅ | | Premium sources ([MS SQL](ecosystem/ms-sql)) and destinations ([Iceberg](ecosystem/iceberg), [Delta](ecosystem/delta), [Snowflake + Iceberg/Open Catalog](ecosystem/snowflake_plus)) | — | ✅ | ## dlt (OSS) dlt is the open source ingestion library, distributed under Apache 2.0. | Component | Description | How to access it | Get started | |---|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | **dlt library** | The Python pipeline engine: extract, normalize, load. Includes schema inference and evolution, incremental loading, write dispositions, pipeline state, and the `dlt pipeline …` CLI for inspection. | [dlt](https://github.com/dlt-hub/dlt) — Apache 2.0, on [PyPI](https://pypi.org/project/dlt/) | [Intro](../intro.md) · [Tutorial](../tutorial/sql-database.md) · `pip install dlt` | | **Core sources** | Three flexible, generic sources shipped with the library that cover most ingestion scenarios out of the box: | Distributed with [dlt library](https://github.com/dlt-hub/dlt/tree/devel/dlt/sources) | [REST API](../dlt-ecosystem/verified-sources/rest_api) · [SQL Database](../dlt-ecosystem/verified-sources/sql_database) · [Filesystem & Cloud Storages](../dlt-ecosystem/verified-sources/filesystem) | | **Verified sources** | A curated set of dltHub-maintained connectors (for example, Kafka, MongoDB, Postgres CDC, Stripe, Hubspot, …) pulled into your project with `dlt init ` . | [`dlt-hub/verified-sources`](https://github.com/dlt-hub/verified-sources) | [Verified sources docs](../dlt-ecosystem/verified-sources) · `dlt init -l` to list available sources | **dlt is a good fit if:** You want a lightweight, code-first ingestion library, are comfortable managing orchestration, scheduling, and operations yourself, or you need to deploy on-prem, on a VPS, or in any environment where managed cloud solution is not an option. dlt runs anywhere Python runs, with no platform dependency. ## dltHub dltHub is a managed cloud platform for running your dlt pipelines, transformations, and notebooks. You can work with dltHub in two complementary ways: - **Web UI** at [dlthub.app](https://app.dlthub.com) — sign up to deploy, schedule, monitor pipelines, manage profiles, browse datasets. - **Locally, from the CLI or Python** — bootstrap a new workspace in one command: ```sh uvx create-dlthub-workspace my-project ``` This creates a runnable workspace with the AI Workbench, example pipelines, and the [`dlt[hub]`](getting-started/installation.md) extra installed. To add dltHub to an existing project instead, run: ```sh pip install "dlt[hub]" dlthub init ``` Either way, you get the dltHub workspace + dashboard, the AI development tooling (`dlthub ai`, MCP server, AI Workbench), per-source contexts, and the `dlthub` library that adds data quality, transformations, and premium sources/destinations. Every component below is part of dltHub and requires a license. Most components are source-available under their own licenses; all are distributed through the `dlthub` PyPI package or the dltHub repositories. | Component | What it is | How to access it | Get started | |---|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------| | **dltHub Platform** | The hosted Web UI and managed runtime at [dlthub.app](https://dlthub.app/) — deploy and schedule pipelines, monitor runs, manage workspaces and profiles, browse datasets, collaborate. | [dlthub.app](https://app.dlthub.com/) | [Platform](https://app.dlthub.com/) · [Runtime overview](runtime/overview.md) · [Runtime tutorial](getting-started/runtime-tutorial.md) | | **AI Toolkits** | The dltHub AI Workbench: a collection of toolkits made of skills, rules, workflows, and MCP wiring that drive agentic pipeline development inside Claude Code, Cursor, and Codex. | [`dlt-hub/dlthub-ai-workbench`](https://github.com/dlt-hub/dlthub-ai-workbench) — source-available under [its own license](https://github.com/dlt-hub/dlthub-ai-workbench/blob/master/LICENSE) | [LLM-native workflow walkthrough](../dlt-ecosystem/llm-tooling/llm-native-workflow.md) | | **dltHub Context** | Per-source LLM contexts (specs, endpoint documentation, prompts) that prime your coding assistant for thousands of APIs. Automatically used by AI Workbench | Browse and copy contexts at [dlthub.com/context](https://dlthub.com/context) | [Build a source with AI](workspace/init.md#llm-native-setup) | | **`dlthub` library** | Python package shipped via `dlt[hub]`. Adds the production capabilities: [data quality](features/quality/data-quality.md), [Python transformations](features/transformations/index.md) (`@dlt.hub.transformation`) and [dbt transformations](features/transformations/dbt-transformations.md), and premium sources/destinations such as [Iceberg / DuckLake](ecosystem/iceberg.md) and [MSSQL Change Tracking](ecosystem/ms-sql.md). | On [PyPI](https://pypi.org/project/dlthub/) | [Installation](getting-started/installation.md) | **dltHub is a good fit if:** You are running pipelines in production, want a coding agent to do the heavy lifting with tooling that supports the generation of production-grade code, need transformations or data quality checks, if you want managed infrastructure, or if you are working as a team. :::tip For tier breakdown (Pro / Scale / Enterprise), see [the intro](intro.md#tiers--licensing). If you have a specific question, feature request, or unique use case, feel free to [reach out](https://dlthub.com/contact). ::: ## Getting started We recommend starting with the [dltHub AI workbench](https://github.com/dlt-hub/dlthub-ai-workbench) to get started quickly while following dltHub best practices.