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Project comparison
MLflow vs Airflow
Compare adoption, momentum, maintenance health, and project basics before choosing which tool to evaluate deeper.
MLflow
The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.
25.9K
Stars
+129
Weekly
88/100
Health
Airflow
Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
45.4K
Stars
0
Weekly
71/100
Health
Quick Take
Adoption
Airflow has the larger GitHub footprint with 45.4K stars.
Momentum
MLflow is currently growing faster at +129 stars this week.
Maintenance
MLflow has the stronger health score at 88/100.
Side-by-side Metrics
| Signal | MLflow | Airflow |
|---|---|---|
| GitHub stars | 25.9K | 45.4K |
| Weekly growth | +129 | 0 |
| Health score | 88 | 71 |
| Contributors | 1.0K | 4.4K |
| Commits per week | 112.2 | 181.2 |
| Open issues | 2.1K | 1.7K |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last commit | 15h ago | 15h ago |
| Last release | v3.12.0 | helm-chart/1.21.0 |
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