Deepchecks
Active learning tools software
AIOps tools
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$89 per model
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What is Deepchecks
Deepchecks is an open-source and commercial toolkit for testing, validating, and monitoring machine learning models and data. It is used by data science and ML engineering teams to detect data quality issues, distribution shifts, model performance degradation, and other risks across training and production. The product provides prebuilt checks and reporting workflows that can be integrated into notebooks, CI pipelines, and monitoring processes. It focuses on ML-specific observability and validation rather than human-in-the-loop labeling workflows.
Broad ML validation checks
Deepchecks includes a library of predefined checks for common ML risks such as data integrity problems, train/serve skew, drift, and performance regressions. This helps teams standardize validation across projects without building every test from scratch. The checks are designed to be run both pre-deployment (testing) and post-deployment (monitoring). It fits teams that want repeatable, code-driven quality gates.
Developer-friendly integration options
The toolkit is designed to run in Python workflows, including notebooks and automated pipelines. This supports integration into CI/CD processes for ML where tests run on new datasets or model versions. Outputs can be used as artifacts for review and governance. The approach aligns with engineering-led ML operations rather than UI-only workflows.
Open-source accessibility
Deepchecks has an open-source foundation that allows teams to evaluate capabilities without an upfront vendor commitment. Organizations can inspect and extend checks to match internal policies and domain constraints. This can reduce lock-in for core validation logic compared with fully proprietary platforms. It also supports experimentation in smaller teams before scaling to managed offerings.
Not a labeling platform
Deepchecks focuses on validation and monitoring, not on annotation management, workforce orchestration, or active-learning-driven labeling queues. Teams needing end-to-end data labeling operations typically require additional tooling. As a result, it may sit alongside, rather than replace, products centered on dataset curation and annotation workflows. Integration work may be needed to connect findings to labeling actions.
Operationalization requires engineering
While checks are provided, production-grade monitoring and alerting still require decisions about thresholds, scheduling, storage, and incident workflows. Teams may need to build surrounding infrastructure to run checks continuously and route results to their observability stack. This can be heavier for organizations without mature MLOps practices. The value depends on consistent adoption in pipelines.
Coverage varies by modality
Deepchecks is strongest in structured/tabular and general ML validation patterns, but depth can vary across specialized modalities and bespoke metrics. Some use cases require custom checks, feature attribution analysis, or domain-specific evaluation that is not available out of the box. Teams working with complex vision or multimodal pipelines may need additional components. This can increase setup time compared with more vertically focused tools.
Plan & Pricing
Main product (LLM Evaluation / Platform) — Cloud-Hosted & Privately-Hosted
| Plan | Price | Key features & notes |
|---|---|---|
| Basic | No public list price (Free trial available) | Up to 3 seats; 1 AI application; up to 5K DPUs/month; 3 months data retention; unlimited prompt-based metrics; multi-lingual AI applications. |
| Scale | Custom pricing (request demo) | All Basic features + 5 seats; 3 AI applications; 20K DPUs/month; premium support; premium compliance; guided platform onboarding. |
| Enterprise | Custom pricing (contact sales) | All Scale features + custom seats & AI applications; custom DPUs/month; enterprise-grade security; enterprise support package; dedicated customer success team. |
Monitoring (Monitoring Pricing page)
Pricing model: Mixed — Open-source (free) + paid plans per-model / custom Free tier/trial: Open-source monitoring available (free). Basic platform shows a free trial is available. Example costs:
- Open-Source: Free (AGPL testing package; 1 model per deployment; community support).
- Startup Plan: $159 per model (standard). Limited-time offer listed: $89 per model.
- Dedicated / Partnership: Custom pricing (get a quote / book a demo). Discount/options: Not specified on the official pages.
Seller details
Deepchecks Ltd.
Tel Aviv, Israel
2021
Private
https://deepchecks.com/
https://x.com/deepchecks
https://www.linkedin.com/company/deepchecks
