Comparison
Prefect vs Airflow: which orchestrator should a data team use?
Prefect 3 and Apache Airflow 3 compared for data pipelines: how you write workflows, scheduling and event triggers, dynamic work, deployment and managed options, observability, ecosystem and when each one fits.
Hemant Walchale
· 3 min read
Prefect and Apache Airflow both schedule and monitor data pipelines written in Python. Airflow is the long-standing default with the biggest ecosystem; Prefect was built later to make orchestration feel like writing normal Python. Both have had major releases recently (Airflow 3 and Prefect 3), so older comparisons are often out of date.
The short answer
- Choose Airflow if you want the widest set of integrations, a managed service from your cloud provider (Amazon MWAA, Google Cloud Composer, Astronomer), and engineers you can hire who already know it.
- Choose Prefect if your team writes Python first, your workflows are dynamic or event-driven, and you want less framework between your code and production.
- Either is fine for a typical nightly batch of extract, load and dbt runs. Team familiarity and hosting usually decide it.
How they compare
| Apache Airflow 3 | Prefect 3 | |
|---|---|---|
| Authoring | DAGs of tasks, with the TaskFlow API (@dag, @task) or operators |
Python functions decorated with @flow and @task |
| Dynamic work | Dynamic task mapping | Native Python loops and conditionals at run time |
| Scheduling | Cron and timetables, asset-based scheduling, external event triggers (asset watchers) | Cron, interval and RRule schedules, events and automations |
| Versioning | DAG versioning: each run is tied to the code it started with | Deployments versioned with your code |
| Backfills | Managed by the scheduler, started from the UI or API | Re-run flows with parameters |
| Failure handling | Retries, callbacks, trigger rules | Retries, caching, transactions that can roll back side effects |
| Integrations | Very large set of provider packages | Integration libraries for major clouds and tools |
| Running it | Scheduler, API server, metadata database, workers or executors | Prefect server or Prefect Cloud, plus workers and work pools |
| Managed options | MWAA, Cloud Composer, Astronomer, others | Prefect Cloud |
| Licence | Apache 2.0 | Apache 2.0 |
What usually decides it
- Who writes the pipelines. Analytics engineers and data scientists tend to find Prefect quicker to pick up, because a flow is just a decorated function you can run locally. Platform teams with many contributors often prefer Airflow’s explicit DAG structure.
- Where you run. If you are on AWS or Google Cloud and want the cloud provider to run the orchestrator, Airflow is the path of least resistance. If you want a managed control plane with your own workers, Prefect Cloud fits.
- How static the work is. Fixed nightly DAGs suit Airflow well. Workflows that branch, fan out over unknown inputs or start when files or events arrive are simpler in Prefect, although Airflow 3 narrowed this gap.
- What you already run. Migrating hundreds of working DAGs rarely pays for itself. Teams more often upgrade to Airflow 3 than switch.
Do you need either?
If your stack is mostly on one platform, its built-in scheduler may be enough: Lakeflow Jobs on Databricks, tasks on Snowflake, Data Factory pipelines in Microsoft Fabric, or dbt Cloud jobs. A separate orchestrator earns its keep when pipelines span several systems.
The short version
Airflow for ecosystem, managed cloud services and hiring; Prefect for Python-first teams with dynamic or event-driven workflows. Both are Apache 2.0, both are mature, and the move between them is rarely worth it on its own.
Related: dbt vs Lakeflow Declarative Pipelines and Databricks vs Microsoft Fabric. Our data platform engineering service designs and runs pipelines like these.
Sources
- Apache Airflow 3 is generally available, Apache Airflow blog
- Introducing Prefect 3.0, Prefect
- Prefect and Apache Airflow repositories, GitHub (licences)
Further reading from other practitioners
Not sure where your own platform stands? Take the free 3-minute data platform scorecard. Unfamiliar terms are explained in the data engineering glossary.