Evidently AI
8.3#2 in machine learning model monitoring softwareFrom $80 a monthFree planWebAPILinuxmacOS
VisitEvidently AI vs STAMM
What both publish
| Fact | Evidently AI | STAMM |
|---|---|---|
| Integrations | Documented integration examples cover Jupyter and Colab, Streamlit, MLflow, DVCLive, Airflow, Metaflow, FastAPI, PostgreSQL, Prefect, Grafana, Prometheus and AWS SES. | 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. |
| Purpose | Evidently evaluates, tests, and monitors LLMs, RAG applications, AI agents, and machine-learning models. | STAMM is an open-source MLOps framework for deploying, monitoring, and maintaining machine-learning soft sensors in industrial processes. |
| Support | Community support is provided through Discord, GitHub issue reporting, documentation, courses and direct email contact. | The project page lists David Camilo Corrales at INRAE, Toulouse Biotechnology Institute, as a contact. |
Evidently AI plans
- DeveloperFree
- Open-sourceFree
- Pro$80 a month
- EnterprisePriced on request
- StartupsPriced on request
STAMM plans
No prices published.
Pick Evidently AI if…
It suits teams evaluating or monitoring AI applications and predictive ML models, including those that want open-source tooling or a hosted platform. Its synthetic-data and red-team capabilities may also suit teams assessing adversarial inputs and risks.
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.