Deepchecks
8.1#3 in machine learning model monitoring softwareFree planFree planWebAPILinuxmacOS
VisitDeepchecks vs STAMM
What both publish
| Fact | Deepchecks | STAMM |
|---|---|---|
| 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. |
Deepchecks plans
- Open-Source MonitoringFree
- Startup MonitoringPriced on request
- Dedicated MonitoringPriced on request
- EnterprisePriced on request
- BasicPriced on request
- Partnership MonitoringPriced on request
STAMM plans
No prices published.
Pick Deepchecks if…
Deepchecks suits teams that need to evaluate and monitor AI systems in production, compare versions, and investigate application behavior. Its deployment options and listed security provisions may also suit organizations with specific infrastructure or compliance needs.
Pick STAMM if…
STAMM is intended for process modelers, ML engineers, operators, and project or production managers working with industrial soft sensors. It may suit teams that want drift monitoring and model tracking in live systems.