SAGE vs Lime

Side-by-side — pricing, strengths, tradeoffs, and fit.

SAGE

Global feature importance calculation using Shapley values.

Lime

Explains black box classifiers with two or more classes.

At a glance

Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
◌ Declining·From $0

Overview

One-liner

Global feature importance calculation using Shapley values.

One-liner

Explains black box classifiers with two or more classes.

Maturity

Declining

Maturity

Declining

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams working on interpretable machine learning who need global feature importance calculations.
Projects requiring detailed explanations of model behavior and feature impact.

✓ Best 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.

Not ideal for

✕ Not for

Real-time applications where quick computation is critical, as Shapley value calculation can be computationally expensive.
Applications that do not require a deep understanding of feature importance or model interpretability.

✕ Not for

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.

Strengths

↑ Strengths

Calculates global feature importance using Shapley values.
Provides insights into the impact of features on model predictions.
Open-source and MIT licensed.

↑ Strengths

Explains black box models
Works with any classifier
Provides local explanations for individual predictions

Weaknesses

↓ Weaknesses

Performance issues with large datasets

↓ Weaknesses

Limited support for real-time model monitoring and updates
Performance overhead for large datasets or complex models
Full SAGE profile →Full Lime profile →
SAGE vs Lime | AI Navigator