Airflow vs Prefect: What's the Difference?
Airflow is the battle-tested enterprise standard — mature, widely adopted, with 1000+ operators and deep Kubernetes support. Prefect is cloud-native with a simpler local dev experience and better dynamic task mapping. Both orchestrate Python workflows; they differ in philosophy, ecosystem, and operational overhead.
Side-by-Side Comparison
Apache Airflow
- • Open source (Apache 2.0), backed by Apache Foundation
- • DAGs defined as Python files with static task graphs
- • 1000+ provider operators (Snowflake, S3, dbt, Spark...)
- • Self-hosted by default; managed via Astronomer/MWAA/Composer
- • Battle-tested at Airbnb, Twitter, LinkedIn
- • Steeper initial setup, high operational maturity
Prefect
- • Open source (Apache 2.0) with Prefect Cloud offering
- • Flows defined as decorated Python functions
- • Dynamic task mapping — map over runtime data
- • Hosted UI (Prefect Cloud) with free tier available
- • Simpler local dev: run flows like regular Python scripts
- • Smaller operator ecosystem, newer community
Mental Model
Think of Airflow as the Linux of orchestrators — powerful, ubiquitous, and configurable to the extreme, but with real setup complexity. Think of Prefect as macOS — opinionated, smoother out of the box, but with more constraints on how you structure workflows. Most enterprise data teams run Airflow because it integrates with everything. Most data science teams prefer Prefect because it feels like regular Python.
When to Use Each
Choose Airflow when:
- • You need 10+ integrations (Snowflake, dbt, Spark, S3)
- • Your team is already using it or hiring for it
- • You're deploying to Kubernetes at scale
- • You need fine-grained task-level SLAs and retries
Choose Prefect when:
- • Your workflows are dynamic (map over lists at runtime)
- • You want hosted UI without managing infrastructure
- • Your team is Python-first, not DevOps-heavy
- • You're building ML or data science pipelines
How They Work Together
Some teams run both: Airflow for critical ETL and warehouse pipelines, Prefect for ML workflows and experimentation. A Prefect flow can be triggered by an Airflow task using a simple HTTP call:
# Airflow task that triggers a Prefect flow
@task()
def trigger_prefect_flow():
import requests
requests.post(
'https://api.prefect.cloud/api/deployments/{id}/create_flow_run',
headers={'Authorization': f'Bearer {PREFECT_TOKEN}'}
)
Common Mistakes
Migrating to Prefect just to escape Airflow ops overhead
Prefect Cloud solves the UI/hosting problem but you still need workers running somewhere. The ops overhead shifts, not disappears.
Building dynamic workflows in Airflow
Airflow's dynamic task mapping (added in 2.3) is limited compared to Prefect's. If your workflow shape changes at runtime, Prefect is the better fit.
Treating them as drop-in replacements
Airflow DAGs and Prefect Flows are different concepts with different abstractions. Migrating is a rewrite, not a port.