Databases · head to head
Apache Airflow vs MLflow
Apache Airflow
Databases
Programmatically author, schedule and monitor data workflows in Python
- From
- Free
- Rated
- -
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Apache Airflow self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Apache Airflow covers Pipelines as Python, MLflow covers Experiment tracking.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Apache Airflow and MLflow actually diverge.
| Attribute | Apache Airflow | MLflow |
|---|---|---|
| Pricing model | Open source, no licence fee; managed services billed separately | open-source |
| Platforms | Linux, Docker, Kubernetes, Self-hosted | Web, Python API, REST API |
| Category | Databases | Machine Learning |
| Founded | Unknown | 2018 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Apache Airflow
- Pipelines as Python
- Web UI
- Cloud provider packages
- Jinja templating
- Retries and dependencies
- Extensible operators
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
What people use each for
The jobs each tool is most often brought in to do.
Apache Airflow
- Scheduling nightly ETL where step order and retries matternot MLflow
- Coordinating machine learning training and evaluation runsnot MLflow
- Orchestrating dbt runs alongside extraction and loadingnot MLflow
- Replacing a sprawl of cron jobs with dependencies and visible run historynot MLflow
MLflow
- Machine learningnot Apache Airflow
- Data analysisnot Apache Airflow
- Model trainingnot Apache Airflow
- Predictive analyticsnot Apache Airflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Apache Airflow
- Self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job
- Built for scheduled batch work, and a poor fit for event-driven or sub-minute latency pipelines
- Because DAGs are Python that the scheduler parses continuously, expensive top-level code in a DAG file slows the whole scheduler
- Local development and testing of DAGs is awkward compared with newer orchestrators designed with it in mind
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
Pricing, plan by plan
Apache Airflow
Free- Apache AirflowFree
- Full scheduler and web UI
- All provider packages
- No task or DAG limits
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Apache Airflow if
- You need pipelines as python.
- You want to start without paying.
- You work on Linux, Docker, Kubernetes, Self-hosted.
- You also want web ui.
Choose MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
Questions people ask
- Is Apache Airflow or MLflow better?
- Neither clearly leads. Apache Airflow starts at Free and MLflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Airflow or MLflow?
- Apache Airflow starts at Free and MLflow at Free.
- Does Apache Airflow or MLflow run on more platforms?
- Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. MLflow runs on Web, Python API, REST API.
- Can I use Apache Airflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Apache Airflow best used for?
- Apache Airflow is most often used for scheduling nightly etl where step order and retries matter, coordinating machine learning training and evaluation runs, orchestrating dbt runs alongside extraction and loading, replacing a sprawl of cron jobs with dependencies and visible run history. Of those, scheduling nightly etl where step order and retries matter and coordinating machine learning training and evaluation runs are not what MLflow is typically brought in for.
- What can Apache Airflow do that MLflow cannot?
- Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
Apache Airflow: Is Apache Airflow free?
Yes. Airflow is open source under the Apache Software Foundation with no licence fee. Costs are the infrastructure to run it, or a managed service such as Google Cloud Composer or Amazon MWAA.
MLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
SourceApache Airflow: What language are Airflow workflows written in?
Python. A workflow is a Python file, so standard language features including loops and datetime handling can generate tasks dynamically, with no XML or command-line configuration.
MLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
SourceApache Airflow: Is Airflow suitable for real-time pipelines?
Not really. Airflow is designed for scheduled batch orchestration. Event-driven or sub-minute work is better served by a streaming platform such as Kafka or a purpose-built streaming engine.
MLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
SourceApache Airflow: What are the main alternatives to Airflow?
Dagster and Prefect are the two most commonly weighed against it, both newer and both designed around the local development and testing experience Airflow is criticised for.
MLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
SourceMLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
SourceRelated pages
More on Apache Airflow
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- MLflow vs NATS
- MLflow vs DuckDB
- MLflow vs MariaDB
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- MLflow vs Knack
- MLflow vs LanceDB
- MLflow vs Marqo
- MLflow vs Nile
- MLflow vs Ninox
- MLflow vs Presto
- MLflow vs Comet ML
- MLflow vs Weights & Biases
- MLflow vs Neptune.ai
- MLflow vs ClearML
- MLflow vs DVC
- MLflow vs Kubeflow
- MLflow vs BentoML
- MLflow vs AWS SageMaker
- MLflow vs DataRobot
- MLflow vs Seldon
- MLflow vs Azure Machine Learning
- MLflow vs Dataiku
- MLflow vs Palantir Foundry
- MLflow vs Pinecone
- MLflow vs Python
- MLflow vs PyTorch
- MLflow vs scikit-learn
- MLflow vs Apache Spark MLlib