Side by side
Arthur 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
Arthur combines agent discovery, observability, and policy controls with hosted, on-premises, and hybrid deployment choices. The Free plan has defined use-case, project, and retention limits; Enterprise pricing requires a custom quote.
Visit Arthur 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
| Arthur | 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 | Premium: $60 a month | Not published |
| Plans published | 3 | None |
| Maker | Not published | Not published |
| Website | arthur.ai | stamm.inrae.fr |
| On machine learning model monitoring software | #7 of 20 | #11 of 20 |
Arthur: what it does well
- Monitors agent activity across several discovery sources.
- Traces tool calls, context, costs, and latency.
- Provides real-time policy verdicts for listed risks.
- Hosted, on-premises, and hybrid deployments are available.
- Free plan includes up to 4 use cases.
What to know first
- Free plan retains data for 7 days.
- Free plan is limited to 2 projects.
- Premium is $60 a month.
- Enterprise pricing is custom.
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.