Alternatives

The Best Evidently Alternatives in 2026

24 alternatives0 free · 20 open source

Looking for something like Evidently? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 20 are open source.

About Evidentlygithub.com/evidentlyai/evidently
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Evidently evaluates, tests, and monitors machine learning systems and data pipelines.

PricingNot confirmed
Platforms—

Top picks at a glance

Quick comparison

#AlternativePricingOpen sourcePlatforms
1Deepchecks —Yes—
2NannyML —Yes—
3Eurybia —Yes—
4Adaptive Reliability Layer —Not stated—
5DVC —Not statedWeb
6Kubeflow Pipelines —Yes—
7Tpot —Yes—
8MLRun —Yes—
9ZenML —Yes—
10Kedro —Not stated—
24 shown

All Evidently alternatives

1

Deepchecks

github.com/deepchecks/deepchecksOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

Deepchecks tests and validates machine learning models and data, covering workflows from research through production.

2

NannyML

github.com/NannyML/nannymlOpen source
Also covers ai infrastructure, data science, and model monitoring · same kind of open-source project

NannyML provides Python tools for post-deployment data science and monitoring model performance under data drift.

3

Eurybia

github.com/MAIF/eurybiaOpen source
Also covers ai infrastructure, machine learning, and model monitoring · same kind of open-source project

Eurybia tracks model drift and validates data during machine learning model deployment.

5

DVC

dvc.org
Also covers ai infrastructure, data science, and machine learning

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.

7

Tpot

github.com/EpistasisLab/tpotOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

TPOT uses genetic programming to optimize machine learning pipelines in Python.

8

MLRun

github.com/mlrun/mlrunOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

MLRun provides an open-source MLOps platform for managing machine learning applications, pipelines, and CI/CD delivery.

9

ZenML

github.com/zenml-io/zenmlOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

ZenML provides a platform for machine learning pipelines and AI agents.

10

Kedro

github.com/quantumblacklabs/kedro
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

11

Ray

github.com/ray-project/rayOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

Ray provides an AI compute engine with a distributed runtime and libraries for machine learning workloads.

12

Datumbox

github.com/datumbox/datumbox-frameworkOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

The Datumbox framework is written in Java for developing machine learning and statistical applications.

13

FLAML

github.com/microsoft/FLAMLOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

FLAML provides a fast Python library for automated machine learning and tuning.

14

Feast

github.com/feast-dev/feastOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

The Feast repository describes an open source feature store for AI and machine learning projects.

15

FlexML

github.com/ozguraslank/flexmlOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

FlexML offers an easy to use and flexible automated machine learning library for Python.

16

goro

github.com/aunum/goroOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

The goro project provides a high level machine learning library written for Go.

17

Notebooks

github.com/rlan/notebooksOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

This Docker-based starter kit provides a runtime for machine learning work in Jupyter notebooks.

18

Primehub

github.com/myelintek/primehubOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

PrimeHub offers an open-source platform for managing machine learning operations and workflows.

19

CML

github.com/iterative/cmlOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

CML provides continuous machine learning and CI/CD workflows for machine learning projects.

21

Feast

github.com/gojek/feastOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

The Feast project provides an open source feature store for AI and machine learning.

22

Feathr

github.com/linkedin/feathrOpen source
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

Feathr provides enterprises with a platform for data engineering and AI workflows.

24

Maze

github.com/enlite-ai/maze
Also covers ai infrastructure, data science, and machine learning · same kind of open-source project

Maze provides an applied reinforcement learning framework for developing projects in Python.

No alternatives match this filter.

Alternatives to similar tools

All alternatives
What is the best alternative to Evidently?
Based on features, tags and platforms, the closest alternatives to Evidently are Deepchecks, NannyML, and Eurybia.
Are there open-source alternatives to Evidently?
Yes: Deepchecks, NannyML, Eurybia, and Kubeflow Pipelines.

Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative