Observability & MonitoringLime
Explains black box classifiers with two or more classes.
- Status
- UnmaintainedNo commits in 1853 days.
- Last checked
- Checked · Oct 12, 2026
- License
- Open source
- Pricing
- Free tier · from $0
What it is
Lime is a tool for explaining machine learning models by providing insights into how these models make decisions, particularly useful for understanding complex and opaque models.
Lime stands out by providing local explanations for any machine learning model, making it a versatile tool for enhancing the interpretability of complex models without requiring access to their internal structure.
When it fits — and when it doesn’t
A good fit for
Teams needing to understand complex machine learning models without access to the model's internal workings.
Projects where transparency and explainability are critical for regulatory compliance.
Look elsewhere if
Scenarios requiring real-time explanations of predictions due to computational overhead.
Use cases that do not require understanding individual prediction rationales, focusing solely on overall accuracy.
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited support for real-time model monitoring and updates
Lime focuses on post-hoc explanations rather than continuous model observability
- 02
Performance overhead for large datasets or complex models
Computational complexity increases with the size of input data and model intricacy
What it does well
Explains black box models
Works with any classifier
Provides local explanations for individual predictions
Pricing
- Free tier
- YesOpen source — free to use
- Paid from
- $0
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up Lime
Setup steps, common gotchas and what to check after 30 days.