Apache Airflow vs 
MLflow
Apache Airflow vs MLflow compared for 2026 — features, license, ease of use, performance and which one to choose. Schedule and monitor data pipelines vs Track experiments and ship models without the spreadsheet.
Updated regularly · curated by OpenSourceAI.tech
Apache Airflow vs MLflow at a glance
| Spec | Apache Airflow | MLflow |
|---|---|---|
| Category | ML frameworks & MLOps | ML frameworks & MLOps |
| Type | Workflow orchestration | Experiment tracking |
| License | Apache-2.0 | Apache-2.0 |
| Runs locally | Yes | Yes |
| Primary language | Python | Python |
| Ease of use | Intermediate | Beginner |
| Best for | recurring data and training pipelines that must not silently fail | any team that has lost track of which run produced the good model |
| GitHub stars | 46.1k | 27.1k |
How Apache Airflow and MLflow score
| Criterion | Apache Airflow | MLflow |
|---|---|---|
| Popularity | 4.0 | 3.5 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 3.5 | 5.0 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
Apache Airflow
Airflow schedules the pipelines that feed your models — the standard orchestrator in data engineering.
- The industry standard, with connectors for everything
- Clear visibility into what ran and what broke
- Huge community and plugin ecosystem
MLflow
MLflow records every run, its parameters and its metrics, then packages the winning model for deployment — the open answer to Weights & Biases.
- Self-hostable, no per-seat pricing
- Works with any framework
- Model registry and deployment included
Key differences
Apache Airflow is workflow orchestration, while MLflow is experiment tracking. Apache Airflow leans more intermediate-friendly, whereas MLflow is more suited to beginner users. In short, Apache Airflow fits recurring data and training pipelines that must not silently fail, and MLflow fits any team that has lost track of which run produced the good model.
Which should you choose?
Choose Apache Airflow for recurring data and training pipelines that must not silently fail. Choose MLflow for any team that has lost track of which run produced the good model.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is Apache Airflow or MLflow easier to use?
MLflow is generally the easier of the two to get started with, while Apache Airflow rewards more setup with more control.
Are Apache Airflow and MLflow free?
Apache Airflow is free and open source (Apache-2.0), and MLflow is free and open source (Apache-2.0). Neither charges for the core software.
Can I run Apache Airflow and MLflow locally?
Apache Airflow: yes · MLflow: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Apache Airflow vs MLflow — which should I pick in 2026?
Choose Apache Airflow for recurring data and training pipelines that must not silently fail. Choose MLflow for any team that has lost track of which run produced the good model.
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