Zeno

At a glance

• Editor score
  5.5 / 10
• Pricing
  Free plan
• Best for
  Teams finding systematic failures in AI outputs
• Free plan
  Yes
• Paid from
  None
• Feature attribution
  No
• Facts checked
  28 Sep 2026

• Where it wins

  • Slice Finder automatically surfaces systematic model failures.
  • Compare models and data slices with metrics and interactive charts.
  • Share findings in collaborative reports with visualizations and markdown.

• Where it doesn't

  • It focuses on evaluation and error analysis, not prediction explanations.
  • Feature attribution is not included among its listed capabilities.
  • Counterfactual explanations and fairness analysis are not listed capabilities.

Our verdict on Zeno

Zeno is an interactive platform for evaluating AI systems by exploring datasets alongside model outputs. Teams and researchers can create projects around evaluation data, define task-appropriate views, calculate metrics, and investigate where systems perform poorly. Its supported examples span chatbots, object detection, audio transcription, image classification, and text classification. The emphasis is on understanding performance patterns across examples and tasks, rather than explaining an individual prediction through a specialized explanation method.

The analysis workflow combines metric comparison with slice discovery. Zeno’s Slice Finder automatically surfaces systematic model failures, while interactive charts let users compare models and data slices. Modular or custom views support different data types, so teams can shape how examples appear for a particular task. Findings can then move into collaborative reports that combine interactive visualizations and markdown. This makes Zeno a fit for teams that need to inspect evaluation results together and communicate patterns, not only produce a score. Integrations include EleutherAI LM Evaluation Harness and Ragas; a Python client can create projects and upload datasets and system outputs.

Zeno is open source and has a free plan, with cloud hosting through Zeno Hub or a self-hosted deployment. The Python client provides a programmatic route for bringing evaluation data into projects, while the web interface supports exploration and report authoring. Its category fit has a boundary: the listed capabilities center on dataset exploration, metrics, slices, charts, and reports, rather than feature attribution, counterfactuals, or fairness analysis. Choose Zeno when the priority is finding recurring errors and sharing evaluation results across a team; choose a different tool if the core requirement is a specialized prediction-explanation workflow or fairness analysis.

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Zeno pricing

Zeno fact sheet

Zeno integrations

Zeno lists 2 integrations on its own site.

• EleutherAI LM Evaluation Harness
• Ragas

See all Zeno integrations →

Alternatives to Zeno

• AAIX360A broad, self-hosted Python toolkit for local, global, and time-series model explanations.9.1
• AArize AIProduction-focused explainability pairs feature attribution with fairness monitoring and model comparisons.8.5
• Fiddler AIA tracing and evaluation platform for teams debugging LLM and agent workflows.7.4

See all Zeno alternatives →

Featured on iTechGuides

Zeno is listed in our AI Model Explainability Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 9 Oct 2026 · How we research and update