Apache Airflow vs Dagster: Key Differences & When to Use Each
Comprehensive side-by-side comparison of features, pricing, and metrics
Key Differences
Compare Apache Airflow and Dagster across features, pricing, integrations, and community metrics. Apache Airflow / Dagster.
Feature
Apache Airflow
Workflow
Dagster
Workflow
Side-by-side comparison of developer tools
Platform to programmatically author and schedule workflows
Data orchestrator for machine learning and analytics
GitHub Stars
⭐ 45,276
⭐ 15,427
Contributors
👥 4,323
👥 673
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Python
Python
Features
- • Airflow
- • Apache
- • Apache Airflow
- • Automation
- • Dag
- • Analytics
- • Dagster
- • Data Engineering
- • Data Integration
- • Data Orchestrator
Integrations
No integrations listed
No integrations listed
Momentum Score
94/100Momentum949494
(stable)
94/100Momentum949494
(stable)
Community Health
91/100Health919191
(excellent)
50/100Health505050
(developing)
Maturity Index
88/100Maturity888888
(mature)
45/100Maturity454545
(emerging)
Innovation Score
88/100Innovation888888
(pioneering)
43/100Innovation434343
(evolving)
Risk Score (higher is safer)
94/100Risk949494
(minimal)
53/100Risk535353
(low)
Developer Experience
80/100DX808080
(good)
54/100DX545454
(needs-improvement)
Links
Apache Airflow Strengths
- ✓ More popular (45,276 stars)
- ✓ Larger community (4,323 contributors)
Dagster Strengths
When to Use Apache Airflow vs Dagster
Use Apache Airflow when its strengths align better with your stack and team needs, and choose Dagster when its ecosystem, integrations, or cost profile is a better fit.
Related comparisons
More Comparisons
Data source: GitHub API
Last updated: 5/4/2026