LUML Flow vs Rubicon-ML
LUML Flow
luml.aiRubicon-ML
capitalone.github.ioSide by side
| Figure | LUML Flow | Rubicon-ML |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Linux | — | Yes |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 0 | 1 |
| Spec score · ML Experiment Tracking Software | 7.0 (#27) | 8.3 (#7) |
| Headquarters | Linz, Austria | McLean, Virginia, United States |
| Integrations | The page lists PyTorch, LangGraph, XGBoost, scikit-learn, LlamaIndex, TensorFlow, Anthropic, LightGBM, DSPy, JAX, LangChain, and Keras. | The documentation includes integrations for Git, Prefect, and Scikit-learn. |
| Intended users | The page presents Flow for people tracking predictive ML training and generative AI experiments, including teams that want to share runs through LUML. | The package metadata identifies developers and science or research users as its intended audience. |
| Purpose | Flow is a live experiment tracker for predictive ML and generative AI. | Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way. |
| Support | The Flow page links to documentation and offers a request-demo link. | The project README directs users to open a GitHub issue to report a bug or make a suggestion. |
Plans
In their own words
Rubicon-ML
Rubicon-ML is a free, open-source Python package for capturing model training and execution details. It records inputs, outputs, parameters, and outcomes in searchable projects and experiments, while Git integration can attach repository origin, branch, and commit information for auditability. Its dashboard and widgets let users explore, filter, visualize, compare, and share logged work. Multiple experiments can be logged in parallel, and asynchronous S3 communication keeps network reads and writes from blocking. Storage backends include local files, Amazon S3, in-memory storage, and Weights & Biases. Integrations are provided for Git, Prefect, and Scikit-learn; RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored. Installations use pip or conda-forge. Visualization is an optional pip extra, while conda installs optional extras by default. The dashboard is a Dash app that can be dockerized, and remote logging does not require a hosted server. It is licensed under Apache License 2.0.