Choose Apache Airflow if you want the widely used open-source standard for scheduling and monitoring Python workflows, with a huge set of ready operators for every cloud. Choose Dagster if you want to model pipelines around data assets, with lineage, data quality checks and testing built in. Both are open source at the core; Airflow is community-run under Apache, while Dagster is a company product that's now part of Prefect.
I read the Apache Airflow home page and its What is Airflow docs, then Dagster's documentation overview, its home page and its Dagster vs Airflow page. One line on Dagster's site will surprise many readers: a banner says Dagster is now part of Prefect.
Airflow and Dagster compared
| Apache Airflow | Dagster | |
|---|---|---|
| Core idea | Workflows as code: Dags of tasks | Pipelines built around data assets |
| Language | Python Dags | Python assets |
| Built-in extras | Many operators for Google Cloud, AWS, Azure and more; web UI with logs | Lineage, observability, asset checks, data catalog and quality |
| Ownership | Apache project, created by the community | Dagster, now part of Prefect |
| Managed option | Not covered on the pages I read | Dagster+ Hybrid and Serverless |
| Free option (as of October 2026) | Free and open source | Open source core |
What Airflow does well
Airflow's docs describe an open-source platform for developing, scheduling and monitoring workflows: time-based or event-triggered batch pipelines, model training, and now agentic and LLM-based workloads. Pipelines are Python, so you can generate Dags dynamically, extend operators and template with Jinja.
Its home page stresses scale and reach. A modular architecture uses a message queue to orchestrate any number of workers, and plug-and-play operators run tasks on Google Cloud, AWS, Azure and many other services. The web UI shows the status and logs of every run. The current docs are for Airflow 3.3.2.
What Dagster does differently
Dagster's docs call it a data orchestrator for data engineers with lineage, observability, a declarative programming model and testability built in. Instead of chaining tasks, you define assets, such as a table or a model, and the dependencies between them. Asset checks, partitions, backfills, schedules and sensors sit on top.
Its own Airflow page makes bold claims, including that engineers are twice as productive in Dagster, so treat those as the vendor's view. More useful is the Airlift guide it offers for running Dagster alongside existing Airflow instances, which means you don't have to switch everything at once.
The paid layer is Dagster+. Its docs list an asset catalog, alerts, asset health status and insights there, and the home page describes Dagster+ AI using the context Dagster already has, such as assets, runs, lineage and failures, to help teams diagnose problems. With Compass, it says teams can build governed data agents on top of their workflows.
The ownership news to factor in
Dagster's site now carries a banner saying Dagster is part of Prefect, and its navigation points people looking for agentic orchestration to Prefect. Airflow, by contrast, is an Apache project run by its community. If long-term governance matters to you, that difference is worth weighing alongside the features.
Deciding in five steps
- Need operators for many clouds and services today? Airflow's catalogue is wide.
- Want to see which tables and models depend on each other? Dagster's asset lineage shows it.
- Care about testing pipelines like application code? Dagster puts testability up front.
- Already running Airflow in production? Try Dagster's Airlift approach on one pipeline before migrating.
- Prefer a community-governed project? Airflow's Apache home is a plus.
If your data sits in many places, read about common data integration challenges and these data analytics tools that sit downstream of any orchestrator.
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My advice on Airflow or Dagster
If you need something proven, widely supported and easy to hire for, I'd choose Airflow. If you're building a modern data platform and want lineage, data quality and testing designed in from day one, I'd choose Dagster, while keeping an eye on how its move into Prefect shapes the roadmap. Run one real pipeline in each before you commit your whole stack.
Do you make a data orchestration or data engineering tool? Tool makers can ask to be considered for a featured place in comparisons like this one.
Frequently asked questions
Is Dagster part of Prefect?
Dagster's own website shows a banner saying Dagster is now part of Prefect.
Is Apache Airflow free?
Yes. Airflow is an open-source Apache project, free to use as of October 2026.
What is an asset in Dagster?
Dagster builds pipelines around assets, such as tables or models, and tracks the dependencies and lineage between them.
Can Dagster run alongside Airflow?
Dagster offers an Airlift guide for working with existing Airflow instances, so you can move gradually.
What can Airflow orchestrate?
Airflow's docs list batch data pipelines, machine learning model training and agentic or LLM-based workloads.
