--- title: "dagster vs prefect" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dagster-io-dagster-vs-prefecthq-prefect" tools: ["dagster-io-dagster", "prefecthq-prefect"] --- # dagster vs prefect *GraphCanon updated Aug 10, 2026* ## Verdict Pick dagster if dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows; pick prefect if prefect is a Python-based workflow orchestration framework designed to create resilient data pipelines. It offers a straightforward approach to building and managing workflows using Python syntax. [dagster](https://dagster.io) reports 16k GitHub stars, 2.2k forks, and 2.6k open issues, last pushed Aug 9, 2026. [prefect](https://prefect.io) has 24k stars, 2.4k forks, and 821 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [dagster's repository](https://github.com/dagster-io/dagster) and [prefect's repository](https://github.com/PrefectHQ/prefect). | | [dagster](/tools/dagster-io-dagster.md) | [prefect](/tools/prefecthq-prefect.md) | | --- | --- | --- | | Tagline | An orchestration platform for data assets | Workflow orchestration framework for resilient data pipelines | | Stars | 15,949 | 23,541 | | Forks | 2,232 | 2,442 | | Open issues | 2,596 | 821 | | Language | Python | Python | | Adopt for | Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. | Prefect is a Python-based workflow orchestration framework designed to create resilient data pipelines. It offers a straightforward approach to building and managing workflows using Python syntax. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Data & Retrieval, Evaluation & Observability | Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dagster](/tools/dagster-io-dagster.md) | [prefect](/tools/prefecthq-prefect.md) | | --- | --- | --- | | Open issues (now) | 2.6k | 821 | | Full report | [trust report](/tools/dagster-io-dagster/trust.md) | [trust report](/tools/prefecthq-prefect/trust.md) | ## Shared compatibility - **Python**: [dagster](/tools/dagster-io-dagster.md) - Python runtime; [prefect](/tools/prefecthq-prefect.md) - Python runtime ## Decision facts: dagster - **Adopt for:** Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. ## Decision facts: prefect - **Pricing:** freemium - Prefect is initially available under an open-source license (Apache-2.0), but commercial support options are also provided for users who require additional services or features. - **Requirements:** Requires Python 3.10+ - **Adopt for:** Prefect is a Python-based workflow orchestration framework designed to create resilient data pipelines. It offers a straightforward approach to building and managing workflows using Python syntax. ## Choose when ### Choose dagster if… - Tags unique to dagster: data-orchestrator, etl, mlops, workflow. - Also covers Data & Retrieval, Evaluation & Observability. - When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### Choose prefect if… - Pricing: Prefect is initially available under an open-source license (Apache-2.0), but commercial support options are also provided for users who require additional services or features.. - Requirements: Requires Python 3.10+. - Tags unique to prefect: ml-ops, observability, workflow-engine. - Also covers Developer Tools. - prefect ships Docker support for self-hosted deployment. - - When your project relies on the latest features in Python, as Prefect supports Python 3.10+ ## When NOT to use dagster - If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements. - In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem. - For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding. - If specific features or integrations crucial to your workflow are found lacking in comparison to competitors. ## When NOT to use prefect - - If your team is already deeply invested in another language for scripting or orchestrating data pipelines (for example, if you prefer a non-Python solution), Prefect might not be the right fit - - For projects where minimal overhead and quick setup are critical, as Prefect's setup requires installing specific dependencies and adhering to its Python-based framework ## Common questions ### What is the difference between dagster and prefect? dagster: An orchestration platform for data assets. prefect: Workflow orchestration framework for resilient data pipelines. See the comparison table for live GitHub stats and shared categories. ### When should I choose dagster over prefect? Choose dagster over prefect when Tags unique to dagster: data-orchestrator, etl, mlops, workflow; Also covers Data & Retrieval, Evaluation & Observability; When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### When should I choose prefect over dagster? Choose prefect over dagster when Pricing: Prefect is initially available under an open-source license (Apache-2.0), but commercial support options are also provided for users who require additional services or features.; Requirements: Requires Python 3.10+; Tags unique to prefect: ml-ops, observability, workflow-engine; Also covers Developer Tools; prefect ships Docker support for self-hosted deployment; - When your project relies on the latest features in Python, as Prefect supports Python 3.10+. ### When should I avoid dagster? If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements. In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem. For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding. If specific features or integrations crucial to your workflow are found lacking in comparison to competitors. ### When should I avoid prefect? - If your team is already deeply invested in another language for scripting or orchestrating data pipelines (for example, if you prefer a non-Python solution), Prefect might not be the right fit - For projects where minimal overhead and quick setup are critical, as Prefect's setup requires installing specific dependencies and adhering to its Python-based framework ### Is dagster or prefect more popular on GitHub? prefect has more GitHub stars (23,541 vs 15,949). Stars measure visibility, not whether either tool fits your constraints. ### Are dagster and prefect open source? Yes - both are open-source projects on GitHub (dagster: Apache-2.0, prefect: Apache-2.0). ### Where can I find alternatives to dagster or prefect? GraphCanon lists graph-backed alternatives at [dagster alternatives](/tools/dagster-io-dagster/alternatives) and [prefect alternatives](/tools/prefecthq-prefect/alternatives) ([dagster markdown twin](/tools/dagster-io-dagster/alternatives.md), [prefect markdown twin](/tools/prefecthq-prefect/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dagster-io-dagster-vs-prefecthq-prefect.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dagster or prefect? dagster: Very active. prefect: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for dagster and prefect? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dagster trust report](/tools/dagster-io-dagster/trust); [prefect trust report](/tools/prefecthq-prefect/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dagster-io-dagster`](/api/graphcanon/graph?tool=dagster-io-dagster) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_