Both tools work. Picking wrong costs months. Here’s the honest breakdown after running both in production.
When Airflow wins
- Large org, dedicated data platform team
- Need for enterprise RBAC, audit logs, compliance
- Already invested in Helm charts and Kubernetes
# Airflow 2.8 TaskFlow API — actually pleasant now
from airflow.decorators import dag, task
from datetime import datetime
@dag(schedule="@daily", start_date=datetime(2025, 1, 1))
def my_pipeline():
@task
def extract():
return [1, 2, 3]
@task
def transform(data: list):
return [x * 2 for x in data]
transform(extract())
my_pipeline()When Prefect wins
- Small-to-medium team that wants shipping over ops
- Dynamic workflows (number of tasks known only at runtime)
- Need Python-native deployments without YAML sprawl
from prefect import flow, task
@task
def extract():
return [1, 2, 3]
@flow
def my_pipeline():
data = extract()
return [x * 2 for x in data]
if __name__ == "__main__":
my_pipeline()Prefect’s local runner + managed Prefect Cloud is genuinely zero-infrastructure for small teams.
Decision matrix
| Factor | Airflow | Prefect |
|---|---|---|
| Setup time | Hours–days | Minutes |
| Dynamic tasks | Workarounds | Native |
| Self-host cost | High | Low |
| Enterprise features | Strong | Growing |
What went wrong
Tried Prefect at a company with 50+ DAGs already in Airflow. Migration cost exceeded 3 months. Start with the right tool.
Checklist
- Count team size and infra appetite first
- Prototype one pipeline in both tools
- Check if existing dbt integration matters (both work)