Evidently integrations

Overview of the available Evidently integrations.

Evidently is a Python library, and can be easily integrated with other tools to fit into the existing workflows.

Below are a few specific examples of how to integrate Evidently with other tools in the ML lifecycle. You can adapt them for other workflow management, visualization, tracking and other tools.

ToolDescriptionGuide or example

Notebook environments (Jupyter, Colab, etc.)

Render visual Evidently Reports and Test Suites.


Create a web app with Evidently Reports.


Log metrics calculated by Evidently to MLflow.


Log metrics calculated by Evidently to DVC.


Run data and ML model checks as part of an Airflow DAG.


Run data and ML model checks as part of a Metaflow Flow.

FastAPI + PostgreSQL

Generate on-demand Reports for models deployed with FastAPI.

Grafana + PostgreSQL + Prefect

Run ML monitoring jobs with Prefect and visualize metrics in Grafana.


Send email alerts with attached Evidently Reports (Community contribution).


Real-time ML monitoring with Grafana. (Old API, not currently supported).

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