Alibi Detect
Algorithms for outlier, adversarial, and drift detection.
- Status
- Generally available
- Last checked
- Checked · Oct 5, 2026
- License
- Open source
- Pricing
- Free tier
What it is
Alibi Detect is an open-source library designed for detecting anomalies, adversarial attacks, and data drift in machine learning models. It supports various data types, including tabular, text, images, and time-series data. The library provides a range of algorithms for unsupervised and semi-supervised learning, making it versatile for different detection tasks. Alibi Detect is implemented primarily in Jupyter Notebook, facilitating easy experimentation and integration into data science workflows.
Comprehensive detection library supporting multiple data types and detection methods.
When it fits — and when it doesn’t
A good fit for
Data scientists monitoring model drift
Provides robust tools for detecting data drift in real-time
Developers working with adversarial machine learning
Includes specific algorithms for adversarial detection
Look elsewhere if
Organizations requiring commercial support
Lacks a commercial support option due to its open-source nature
Users needing a GUI-based tool
Primarily Jupyter Notebook-based, requiring coding knowledge
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
High number of open issues
146 open issues on GitHub
- 02
Limited documentation
Primarily documented through Jupyter Notebooks
- 03
No explicit license
License field marked as NOASSERTION on GitHub
What it does well
Versatile data type support
Supports tabular, text, images, and time-series data
Open-source and community-driven
Available on GitHub with 2548 stars and 28 contributors
Comprehensive detection capabilities
Includes algorithms for outlier, adversarial, and drift detection
Pricing
- Free tier
- Yes
- Paid from
- —
- Model
- N/A
- Enterprise plan
- No
Pricing last confirmed Sep 27, 2026.
Performance benchmarks
How Fast Is It?
Set up Alibi Detect
Setup steps, common gotchas and what to check after 30 days.