Deepchecks vs STAMM
Deepchecks
deepchecks.comSTAMM
stamm.inrae.frSide by side
| Figure | Deepchecks | STAMM |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | Yes | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 8 | 0 |
| Spec score · Machine Learning Model Monitoring Software | 8.9 (#2) | 7.1 (#14) |
| Deployment | Deployment options listed include multi-tenant SaaS, virtual private cloud on GCP or Azure, bare metal, and AWS-managed deployment. | It integrates existing soft sensors into live systems alongside physical instruments without requiring rewrites. |
| Installation | The testing library installs with pip or conda and offers NLP and vision extras. | The reference deployment uses Docker Compose and lists Linux, macOS, or Windows with Docker 24 or later and Docker Compose as requirements. |
| Integrations | The integrations page describes Deepchecks integrations with pytest, Apache Airflow, ZenML, and CML. | The workflow orchestrator calls the model registry over REST, and the project describes data ingestion through equipment REST hooks, MQTT applications, or frameworks such as LEAF. |
| License | The deepchecks and monitoring projects are AGPL 3.0, while monitoring enterprise components under the ee directory use a commercial license. | STAMM is released under the Apache License 2.0. |
| Security and compliance | Deepchecks states support for SOC 2 Type 2, GDPR, HIPAA, SSO and AWS GovCloud. | The project describes a FAIR-aligned YAML metadata schema for soft-sensor models that can link to FAIRDOM-SEEK catalogues such as the IBISBA Knowledge Hub. |
Plans
Deepchecks
- BasicFree
- Open-Source MonitoringFree
- Startup MonitoringPriced on request
- BasicPriced on request
- Dedicated MonitoringPriced on request
- EnterprisePriced on request
- Partnership MonitoringPriced on request
- ScalePriced on request
In their own words
Deepchecks
Deepchecks has no write-up on Specifiction yet; its figures are above.
STAMM
STAMM is an open-source MLOps framework for deploying and maintaining machine-learning soft sensors in industrial processes. It monitors live process data for concept drift and changes in operating regimes, and its registry records model versions, configurations, artifacts, validation results, and metadata. A dashboard shows process measurements, soft-sensor outputs, drift signals, and historical context, with support for human-in-the-loop labelling. STAMM serves Python and R soft sensors through REST inference. Its event-driven orchestrator calls the registry over REST and links predictions to the data snapshot that produced them. The drift detector package offers 10 detectors through a common interface and can also be used separately. Data can enter through equipment REST hooks, MQTT applications, or frameworks such as LEAF. InfluxDB is the reference time-series store; a PostgreSQL adapter is described as in progress. The reference Docker Compose installation supports Linux, macOS, or Windows with Docker 24 or later, and 8 GB RAM or more is recommended. Released under Apache License 2.0, STAMM is intended for process modelers, ML engineers, operators, and production managers.