Dagster models pipelines around the data they produce rather than the tasks they run — which makes lineage and testing far easier than in Airflow.
| Category | ML frameworks & MLOps |
| Type | Data orchestration |
| License | Apache-2.0 |
| Runs locally | Yes |
| Built with | Python |
| Skill level | Intermediate |
| Best for | teams who want their pipelines testable and their lineage visible |
Other open-source ml frameworks & mlops tools worth comparing:
TensorFlowGoogle's deep-learning framework, built for production
PyTorchThe framework nearly every modern AI model is written in
OpenCVThe computer vision library everything else builds on
scikit-learnClassical machine learning, done properly
Apache AirflowSchedule and monitor data pipelines
RayScale Python from a laptop to a cluster
JAXNumPy with autodiff, JIT and TPUs
XGBoostStill the one to beat on tabular data
Label StudioLabel anything — text, images, audio, video
MLflowTrack experiments and ship models without the spreadsheet
ONNXMove a model between frameworks and runtimes
LightGBMGradient boosting that trains fast on big tables
CVATSerious annotation for computer vision
DVCGit for datasets and models
OptunaFind the right hyperparameters without guessingDagster is free and open-source (Apache-2.0 license), so you can use, self-host and modify it at no cost.
Yes. Dagster is designed to run on your own machine or server, keeping your data private.
Popular open-source alternatives include TensorFlow, PyTorch, OpenCV. See the comparisons above to choose.
Browse the full directory of open-source AI tools, models and projects — updated daily.
Browse all tools →