Prefect or Airflow?

Instead of manually coding task dependencies and using complex sensors, write Python and let Prefect's event-based and dynamic architecture do the rest

Native Task Dependency Management

Airflow code isn't DRY and requires significant boilerplate config. With @flow decorators on existing Python functions, Prefect structures your workflow order at runtime automatically.

1from prefect import flow
2from datetime import datetime
5def get_datetime():
6    return
9def greetings():
10    now = get_datetime()
11    print(f"Hello, World! It's {now}")
13if __name__ == "__main__":
14    greetings()
Event-based triggers

Prefect Cloud does not rely on clunky sensors—it is event-based at its core. This means flows can run as a response to internal or external webhook events, which are first-class observable objects within the Prefect dashboard.

2  - enabled: true
3    match:
4 prefect.flow-run.*
5    expect:
6      - prefect.flow-run.Completed
7    match_related:
8 prefect.flow.etl-flow
9      prefect.resource.role: flow
10    parameters:
11      param_1: "{{ event }}"
Heterogeneous infrastructure

Instead of managing a cluster of Airflow instances per team, customize infrastructure needs down to each workflow. Prefect allows you to operate lean—cut your infrastructure bill and development time when collaborating with devops.

2- name: deployment-1
3  entrypoint: flows/
4  work_pool: *my_docker_work_pool
5  # Use default Docker build
6  build: *docker_build
8- name: deployment-2
9  entrypoint: flows/
10  work_pool: *my_docker_work_pool
11  build:
12      - prefect_docker.deployments.steps.build_docker_image:
13          <<: *docker_build_config
14          # Override with custom Dockerfile
15          dockerfile: Dockerfile.custom
Cron-based scheduling
Scalable orchestration infrastructure
Retries & logging
Automated task dependencies
Event-based triggers
Built-in notifications
Workflow-specific infrastructure
Cross-task data sharing

Hear from developers who chose Prefect over Airflow

Markus Schmitt

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.

Chas DeVeas

I can go from thought to production 5x faster.

Madison Schott

What would have taken 2-3 months to get running in Airflow took us only 1 month.

Braun Reyes

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.

Chris Jordan

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.

See what others are saying