Dagster vs Prefect

Verdict

Both Dagster and Prefect moved past Airflow's task-and-DAG model, but in different directions. Dagster organizes a pipeline around the assets it produces — tables, files, models — giving every asset a lineage graph, a freshness policy, and typed inputs and outputs it can validate before a run starts. Prefect organizes a pipeline around plain Python functions decorated as tasks and flows, keeping the code itself as close to ordinary Python as possible and letting structure emerge at runtime rather than being declared upfront.

If what you care about most is knowing which table is stale and why, Dagster's model does that natively. If what you care about most is writing pipelines that branch and fan out based on data you don't know the shape of in advance, Prefect's model is a more natural fit.

Choose Dagster if

• You want data lineage and per-asset freshness tracked automatically as part of the orchestration model, not as a separate tool.
• Your team already uses dbt and wants dbt models to sit in the same asset graph as ingestion and ML steps.
• Built-in typing and unit testing for pipeline code matter as much as scheduling it.

Choose Prefect if

• Pipeline structure depends on runtime data, and you want that expressed as ordinary Python control flow rather than a declared asset graph.
• You want automation rules that react to run-state changes (retry, notify, trigger a downstream flow) without extra plumbing.
• You want to run the same code locally, in containers, on Kubernetes, or on serverless infrastructure with minimal change.

What they share

Both are open source with an optional managed cloud control plane (Dagster+ and Prefect Cloud, respectively), both support scheduled and event-driven triggers, and both were built after Airflow specifically to address gaps practitioners hit with the older task-DAG model. Migrating between them, or from Airflow to either, is a real project, not a config change — evaluate on your messiest existing pipeline, not a toy example.