Midcore Tracks vs Rubicon-ML
Midcore Tracks
midcore.aiRubicon-ML
capitalone.github.ioSide by side
| Figure | Midcore Tracks | Rubicon-ML |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 2 | 1 |
| Spec score · ML Experiment Tracking Software | 8.0 (#9) | 8.3 (#7) |
| Founded | 2024 | 1994 |
| Intended users | The maker describes it for teams shipping auditable ML models, needing staged promotion and rollback safety, or operating in regulated industries. | The package metadata identifies developers and science or research users as its intended audience. |
| Purpose | Tracks captures experiments, runs, metrics, and artifacts, registers models, and promotes them to production with signed evidence. | Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way. |
Plans
Midcore Tracks
- Midcore Develop (Squad)Priced on request
- Midcore Enterprise (Business tier and up)Priced on request
In their own words
Midcore Tracks
Midcore Tracks has no write-up on Specifiction yet; its figures are above.
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.