Side by side
Radicalbit AI Monitoring 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.
Free plan APISelf-hostedWeb
Radicalbit AI Monitoring combines data checks, model metrics, drift analysis, and LLM tracing in a free self-hosted package. Check the documented model scope and deployment requirements against your setup.
Visit Radicalbit AI Monitoring Free plan APILinuxMac+3
Choose 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
| Radicalbit AI Monitoring | STAMM | |
|---|---|---|
| Free plan | Yes | 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 | 1 | None |
| Maker | Not published | Not published |
| Website | radicalbit.ai | stamm.inrae.fr |
| On machine learning model monitoring software | #10 of 19 | #11 of 19 |
Radicalbit AI Monitoring: what it does well
- Free and self-hosted under Apache 2.0.
- Checks data quality and tracks model metrics.
- Offers several drift detection methods.
- Compares reference and current datasets.
- Includes a web UI and Python SDK.
What to know first
- Documented scope focuses on classification and regression.
- REST API requires PostgreSQL and distributed storage.
- Spark metric jobs require Kubernetes.
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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