Alibi review
A self-hosted Python library spanning attribution, counterfactuals, and model profiles.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Alibi is a Python library for inspecting and interpreting machine-learning models, aimed at developers and teams that need explanations for classification or regression. It supports black-box and white-box models, including TensorFlow/Keras models, and can be installed with pip or conda. Its methods cover local and global explanations, making it relevant when teams need to investigate individual predictions as well as broader model behavior.
The explanation toolkit ranges from feature attribution methods such as Integrated Gradients, Kernel SHAP, and Tree SHAP to counterfactual explanations, including prototype-guided methods. Anchor explanations work with tabular, text, and image inputs. For broader model inspection, Alibi includes partial dependence, accumulated local effects, and permutation importance. Confidence-related measures include trust scores and linearity measures. This breadth makes the library a fit for teams comparing several explanation approaches; it is less suited to buyers seeking fairness analysis or audit reports, which are not among its listed capabilities.
Alibi is self-hosted rather than a hosted web service, so teams work with it in Python environments and follow an API based on initializing, fitting, and calling an explainer. That favors developer-led workflows over a ready-made interface. Alibi is source-available, and its Business Source License may require a commercial license for commercial use; the license changes to Apache 2.0 four years after each version's release. Pricing uses a contact-sales model. Choose Alibi when the priority is a broad set of model explanation methods under self-hosted control; consider alternatives if you need hosted delivery, fairness analysis, or audit reporting.
Alibi pros and cons
- Where it wins
- Offers local and global explanations for classification and regression
- Includes attribution, anchors, dependence profiles, and counterfactual methods
- Supports black-box, white-box, and TensorFlow/Keras models
- Where it doesn't
- Requires installation and use within Python environments
- Commercial use may require a commercial license
- Pricing is handled through contact with sales
Alibi fact sheet, pricing and score →
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