Prefect is Built Different
After being a lead contributor of the Airflow project and serving on its Product Management Committee (PMC), Jeremiah Lowin saw the limitations of rigid orchestration firsthand. Modern data teams needed more than just scheduled batch jobs—they required dynamic scaling, native data sharing, and unified observability. That's why he built Prefect: to empower teams with Python-native development, complete system visibility, and infrastructure that adapts to your needs, not the other way around.
Predictable Scaling & Cost Control
Run workflows on the right-sized infrastructure, automatically provisioned to match real workload demands—no waste, no manual tuning.
Customers see 60-70% reduction in infrastructure costs.
Ship Faster Without Rewrites
Ship production-ready Python code without rewrites and eliminate data transfer bottlenecks with in-memory passing.
Teams that migrate to Prefect deploy 3-15x 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)Resilient & Reliable
Unify observability across workflows, test locally and recover automatically.
Customers have reduced pipeline failures by 80%
Data Challenges Have Evolved, But Airflow Hasn't
Airflow was built to solve the data challenges of 2014. Over the past decade, data challenges have evolved, but Airflow hasn't. Prefect the the solution designed for the modern data needs of today and tomorrow.
Airflow
Organizations face mounting costs from outdated orchestration. Airflow isn't the solution, it is part of the problem.
Prefect
A fundamentally different approach designed for modern data needs. Stop being blocked by your orchestrator.
Compare Plans
Migrating is Easier Than You Think
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
Why Teams Make the Switch
Predictable Scaling & Cost Control
Run workflows on the right-sized infrastructure, automatically provisioned to match real workload demands—no waste, no manual tuning.
- Pay only for compute you actually use
- Scale automatically from 5 rows to 5 million
- Define infrastructure once, use it anywhere
- Unified control across all environments
Ship Faster Without Rewrites
Simplify workflows with our pythonic orchestrator with built-in task-based data sharing.
- Full support for modern Python features (async/await)
- Standard Python testing tools work out of the box
- Natural dependency management through function calls
- Simplify workflow logic for easier debugging
- Reduce infrastructure costs with in-memory passing
Business Reliability
What works locally, works in production - catch issues before they impact business.
- Reproduce production environments locally for testing
- Catch errors early with type-safe deployments
- Simple workflow registration process
- End-to-end logging and automatic error recovery
Don't Take Our Word For It
Hear what ex-Airflow users have to say when they try Prefect.
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.
Learn More
Explore additional resources that dive into the differences between Prefect and Airflow.
See the Difference
Review the table below to see a side-by-side comparison of Prefect and Airflow's key technical differences.
Prefect vs. Airflow
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