Side by side
Domino Model Monitor vs STAMM
Both stand on machine learning model monitoring software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Domino Model Monitor combines monitoring, alerting, remediation workflows, and deployment choices for organizations managing production models. Its broad framework support and regulated-environment options are useful, but buyers must request pricing and select a support tier.
Visit Domino Model MonitorChoose STAMM if you need an open-source, self-hosted framework to monitor industrial soft sensors and track model versions and results. Its drift signals can identify when maintenance may be needed, but the framework does not prescribe how to rebuild a model.
Visit STAMMThe facts
| Domino Model Monitor | STAMM | |
|---|---|---|
| Free plan | No | Yes |
| Open source | Not published | Not published |
| Runs on | API, Self-hosted, Web | API, Linux, Mac, Self-hosted, Web, Windows |
| Lowest paid plan | Free or on request | Not published |
| Plans published | 3 | None |
| Maker | Not published | Not published |
| Website | domino.ai | stamm.inrae.fr |
| On machine learning model monitoring software | #16 of 20 | #11 of 20 |
Domino Model Monitor: what it does well
- Tracks data drift and model quality
- Central dashboard with customized alerts
- Supports Python, R, SAS, MATLAB, TensorFlow
- Self-hosted and air-gapped deployment options
- Includes model registry and experiment tracking
What to know first
- Paid service with pricing on request
- No free plan
- Support terms vary by plan
- Designed primarily for large enterprises
STAMM: what it does well
- Open-source under Apache License 2.0.
- Monitors live data for drift and operating-regime changes.
- Registry tracks versions, artifacts, validation results, and metadata.
- Dashboard supports historical context and human-in-the-loop labelling.
- Ten drift detectors share a common interface.
What to know first
- A PostgreSQL adapter is still in progress.
- The framework flags possible maintenance needs but does not guide model rebuilding.
- Docker 24 or later and Docker Compose are required for the reference deployment.
- At least 8 GB RAM is recommended.
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