SAGE vs SHAP

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

SAGE

Global feature importance calculation using Shapley values.

SHAP

Game theoretic approach to explain machine learning model outputs.

At a glance

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

Overview

One-liner

Global feature importance calculation using Shapley values.

One-liner

Game theoretic approach to explain machine learning model outputs.

Maturity

Declining

Maturity

Established

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 detailed explanations of individual predictions from complex models
Projects where model transparency and explainability are critical for regulatory compliance
Developers looking to improve the interpretability of their machine learning systems

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

Applications requiring real-time explanation generation with high latency constraints
Scenarios where the computational overhead of SHAP is prohibitive due to large datasets or complex models

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

Game theoretic approach to explain model outputs
Unified measure of feature importance
Supports a wide range of machine learning models
Provides both global and local explanations

Weaknesses

↓ Weaknesses

Performance issues with large datasets

↓ Weaknesses

Limited performance with large datasets
Complex setup for integrating with existing ML pipelines
Full SAGE profile →Full SHAP profile →