You Can't Teach An Old DAG New Tricks
Prefect was founded by one of Airflow's top contributors who saw that modern data workflows needed a fresh approach, so he built a new solution from the ground up. Prefect isn't just an improvement on Airflow—it's orchestration reimagined.
Why teams are switching to Prefect
To view a side-by-side capability comparison between Prefect and Airflow, select the button below.
Strengthen collaboration and trust with enhanced observability
Where Airflow falls short: Limited observability restricts workflow visibility to the creator’s team, making it difficult to foster cross-team collaboration or offer insight to stakeholders.
Why Prefect stands out: Built-in observability and monitoring centralize workflow visibility, offering insight to stakeholders outside of the workflow author. Teams can collaborate without sacrificing control, offering a single-pane-of-glass view into job performance, failures, and alerts, strengthening trust and transparency.
Deploy faster by scaling and deploying workflows independently
Where Airflow falls short: Centralized scheduling can slow down deployment, creating a single point of failure that risks system-wide impacts. Modifying workflows requires adapting code to fit Airflow’s specific structure, adding unnecessary complexity.
Why Prefect stands out: Each workflow is treated as an independent service, enabling teams to deploy, update, and scale without impacting others. Prefect’s pure Python approach also removes the need for retrofitting, allowing workflows to be designed naturally with minimal code changes — so you can deploy workflows to production faster.
1from prefect import flow, task
2
3@task
4def add_one(x: int):
5 return x + 1
6
7@flow
8def main():
9 for x in [1, 2, 3]:
10 first = add_one(x)
11 second = add_one(first)
Cut costs without sacrificing performance
Where Airflow falls short: Airflow requires fixed resource provisioning, meaning teams either overprovision, wasting resources, or underprovision, risking performance.
Why Prefect stands out: Adaptive scaling provides resources on demand, reducing the risk of paying for compute you don't need. This flexibility saves costs without impacting performance, enabling precise budget planning with predictable expenses.
Ship responsive workflows that meet modern-day needs
Where Airflow falls short: Traditional time-based scheduling in Airflow limits workflows to batch processing, which doesn’t align with today’s real-time business demands.
Why Prefect stands out: Event-driven scheduling lets workflows respond to real-time triggers instantly, providing the reliability and agility that modern use cases require, from high-volume event processing to real-time reporting.
Which tool is best for you?
Modern data team challenges have evolved since Airflow was created. Your choice between Prefect and Airflow should align with your team's needs and workflow complexity.
Choose Prefect if you need...
- Modern workflow capabilities (real-time events, dynamic scaling, thousands of concurrent tasks)
- Resource optimization (workflow-specific resources, automatic scaling based on workload)
- Developer productivity (Python-native development, minimal DevOps overhead)
- Team independence (autonomous deployments)
- Scalable collaboration (secure workflow sharing)
Airflow might be sufficient if you...
- Run simple, predictable workflows (static pipelines, scheduled batch jobs)
Have established and expendable DevOps resources for maintenance
- Prefer centralized management and fixed resources
Migration made simple
We understand that switching may be daunting, but rest assured that we’ve designed Prefect with ease of use in mind - this includes our migration and implementation process!
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- ✓ Hands-on migration assistance
- ✓ Dedicated migration documentation
- ✓ Direct access to support engineers
- ✓ Active community guidance
Capability comparison
Review the table below to see a side-by-side comparison of Prefect and Airflow's key technical differences.
Prefect vs. Airflow
Hear from our users
It addresses many of the pain points common to more complicated tools like Airflow. Specifically, Prefect lets you turn any Python function into a task using a simple Python decorator.
I can go from thought to production 5x faster.
What would have taken 2-3 months to get running in Airflow took us only 1 month.
Our Data Engineering Platform used to be a liability. Now it’s a strength. Saving us days on DAG design vs. Airflow.
Airflow is heavier from an infra perspective - from an infra and identity perspective, it was much more bloated and inflexible.
We have been able stay on top of the data flows we've moved to Prefect easily. Seeing failures, successes, and outages in a timely and clear fashion has let us inform stakeholders what's up with the data flows.
Keep learning
Explore additional resources that dive into the differences between Prefect and Airflow.
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- ✓ Up to 5,000 runs daily (see pricing for more information)
- ✓ Full platform access
- ✓ Migration support
- ✓ No credit card required