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Deepchecks

Deepchecks is an open-source (AGPL-3.0) Python library for continuous validation of ML models and data, with test suites for data integrity, drift and model performance, plus a commercial LLM evaluation product.

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Introduction

Deepchecks is a holistic open-source solution for testing and validating ML models and data. Checks are grouped into suites for data integrity, train-test validation and model evaluation, covering issues such as duplicates, label leakage, feature drift and weak segments.

Suites run in notebooks or pipelines and return results with pass or fail conditions, so they can gate training pipelines. Support covers tabular, NLP and computer vision data. Deepchecks the company also offers a commercial LLM evaluation and monitoring platform.

The open-source library has seen slower releases recently (last tagged release December 2024). In the quality loop it is a validation gate for classic ML pipelines.

The source code is at deepchecks/deepchecks on GitHub under the AGPL-3.0 licence, with about 4,100 stars; the most recent commit was on 24 November 2025.

Features

  1. Data integrity checks: Duplicates, mixed types, outliers.
  2. Train-test validation: Drift and leakage.
  3. Model evaluation: Performance and weak segments.
  4. Conditions: Pass or fail thresholds.
  5. Multiple modalities: Tabular, NLP and vision.
  6. HTML reports: Shareable results.

Pros

  1. Ready-made suites: Useful checks out of the box.
  2. Free to use: Open source under the AGPL-3.0 licence, so the core tool costs nothing to use.
  3. Actively maintained: The repository had commits as recent as 24 November 2025.
  4. Community: About 4,100 GitHub stars.

Cons

  1. AGPL licence: Copyleft terms.
  2. Slower releases: Last tagged release was December 2024.
  3. LLM features commercial: LLM evaluation is in the paid product.
from deepchecks.tabular import Dataset
from deepchecks.tabular.suites import full_suite
result = full_suite().run(train_dataset=Dataset(train, label="y"), test_dataset=Dataset(test, label="y"), model=model)
result.save_as_html("deepchecks.html")

Resources:

Pricing

Deepchecks is free and open source under the AGPL-3.0 licence.

  1. ML teams validating tabular or vision pipelines.

Conclusion

Deepchecks offers practical test suites for ML data and models.

Check release activity and licence before standardising on it.

On CheckOps, Deepchecks is filed under the Release & CI gates and Production monitoring & observability stages. Related tools listed here include Evidently, Great Expectations (GX), Giskard, Deequ, DeepEval and Promptfoo. Before standardising on one, compare recent maintenance activity, the pricing model as your usage grows, and the integrations you rely on today. Information on this page was verified against the official website, documentation and repository on 11 November 2026.

Alternatives

Other tools on CheckOps that cover similar ground. Open a page, or compare one with Deepchecks side by side.