Best What-If Tool Alternatives in 2026
The top What-If Tool alternatives are AIX360, Arize AI and Fiddler AI: 15 AI model explainability software our editors would look at instead of What-If Tool, in our ranking order.
What-If Tool: A free, open-source tool for visual model explanations, counterfactuals, and fairness checks. Where it falls short: listed model support centers on TensorFlow and custom prediction functions.
Switching from What-If Tool? For most ai model explainability software buyers the best What-If Tool alternative is AIX360, built for teams needing a broad, free XAI toolkit. On a budget, Arize AI starts at $50/mo.
Why People Look for a What-If Tool Alternative
- Listed model support centers on TensorFlow and custom prediction functions
- No export feature for generating audit reports
- Requires a notebook, TensorBoard, or supported model environment
- No audit reports — 2 of the alternatives below have it
Quick Picks by What You Need
- Best overallAIX3609.1
Teams needing a broad, free XAI toolkit
Visit AIX360 - Lowest priceArize AI8.5
Paid plans from $50/mo
Visit Arize AI - Best free optionAIX3609.1
Has a free plan
Visit AIX360 - Best for audit reportsShapash7.1
Audit reports (What-If Tool: no)
Visit Shapash
All 15 What-If Tool Alternatives, Ranked
Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. How we rank.
-
Best forTeams needing a broad, free XAI toolkit
- Better than What-If Tool:
- Editor score 9.1 vs 7.2
A broad, self-hosted Python toolkit for local, global, and time-series model explanations.
- Counterfactual explanations
- Feature attribution
9.1/10★★★★★Visit AIX360 -
Best forTeams monitoring explainability and fairness in production
- Better than What-If Tool:
- Editor score 8.5 vs 7.2
Production-focused explainability pairs feature attribution with fairness monitoring and model comparisons.
- Fairness analysis
- Feature attribution
8.5/10★★★★☆Visit Arize AI -
Best forTeams diagnosing LLM and agent behavior
- Better than What-If Tool:
- Editor score 7.4 vs 7.2
A tracing and evaluation platform for teams debugging LLM and agent workflows.
7.4/10★★★★☆Visit Fiddler AI -
Best forPractitioners seeking glassbox models and explanations
- Better than What-If Tool:
- Editor score 7.4 vs 7.2
A self-hosted Python toolkit pairing interpretable EBMs with LIME and SHAP explanations.
- Fairness analysis
- Feature attribution
7.4/10★★★★☆Visit InterpretML -
Best forTeams explaining models across varied data types
- Better than What-If Tool:
- Editor score 7.4 vs 7.2
A flexible toolkit for local and global explanations across varied data types.
- Counterfactual explanations
- Fairness analysis
- Feature attribution
7.4/10★★★★☆Visit OmniXAI -
Best forTeams explaining models across frameworks
- Better than What-If Tool:
- Editor score 7.4 vs 7.2
A broad, model-agnostic toolkit for explaining predictive models across R and Python.
- Counterfactual explanations
- Fairness analysis
- Feature attribution
7.4/10★★★★☆Visit DALEX -
Best forTeams combining explanations, fairness, and model assessment
- Better than What-If Tool:
- Editor score 7.4 vs 7.2
A self-hosted toolkit for combining model explanations, fairness checks, and assessment.
- Counterfactual explanations
- Fairness analysis
- Feature attribution
7.4/10★★★★☆Visit Microsoft -
Best forPyTorch teams needing detailed attribution analysis
- Better than What-If Tool:
- Editor score 7.4 vs 7.2
A free, self-hosted toolkit for detailed attribution analysis of PyTorch models.
- Counterfactual explanations
- Feature attribution
7.4/10★★★★☆Visit Captum -
Best forTeams needing Shapley explanations across model types
- Better than What-If Tool:
- Editor score 7.4 vs 7.2
A free, self-hosted Python library for feature attribution across varied model types.
- Fairness analysis
- Feature attribution
7.4/10★★★★☆Explore SHAP -
Best forTeams needing local, global, and counterfactual explanations
- Better than What-If Tool:
- Editor score 7.3 vs 7.2
A self-hosted Python library spanning attribution, counterfactuals, and model profiles.
- Counterfactual explanations
- Feature attribution
7.3/10★★★★☆Contact Sales -
Best forTeams sharing explainability dashboards and reports
- Better than What-If Tool:
- Audit reports
An open-source Python toolkit for exploring, documenting, and serving model explanations.
- Audit reports
- Feature attribution
7.1/10★★★★☆Visit Shapash -
Best forTeams wanting shareable SHAP dashboards for supported models
A free Python library for shareable explanations and performance views of supported models.
- Feature attribution
7.0/10★★★★☆Visit site -
Best forTeams focused on actionable counterfactual examples
A free Python library for exploring alternative predictions with configurable counterfactuals.
- Counterfactual explanations
- Feature attribution
6.9/10★★★☆☆Visit DiCE -
Best forTeams auditing group fairness and bias
- Better than What-If Tool:
- Audit reports
A focused toolkit for group fairness audits, with reports and Python, CLI, and web access.
- Audit reports
- Fairness analysis
6.8/10★★★☆☆Visit Aequitas -
Best forTeams measuring and mitigating group fairness disparities
A focused Python toolkit for measuring group fairness and applying mitigation methods.
- Fairness analysis
6.7/10★★★☆☆Visit Fairlearn
What-If Tool Alternatives: Common Questions
What is the best alternative to What-If Tool?
AIX360: #1 in our AI Model Explainability Software ranking, with an editor score of 9.1 out of 10. A broad, self-hosted Python toolkit for local, global, and time-series model explanations.
Is there a free alternative to What-If Tool?
Yes. AIX360, Arize AI, Fiddler AI, InterpretML and OmniXAI have a free plan or a free tier (14 of the 15 alternatives on this page).
Why do people look for What-If Tool alternatives?
Listed model support centers on TensorFlow and custom prediction functions; No export feature for generating audit reports; Requires a notebook, TensorBoard, or supported model environment.
How do I switch from What-If Tool?
Export your data from What-If Tool (most tools have an export in their settings). Sign up for the alternative; start on its free plan or trial if it has one. Import your export and set up the integrations your team relies on. Run both side by side for a short while, then cancel What-If Tool.
How to Switch from What-If Tool
- Export your data from What-If Tool (most tools have an export in their settings).
- Sign up for the alternative; start on its free plan or trial if it has one.
- Import your export and set up the integrations your team relies on.
- Run both side by side for a short while, then cancel What-If Tool.
What-If Tool vs Each Alternative
| # | Tool | Free plan | Paid from | Feature attribution | Counterfactual explanations | Fairness analysis | Audit reports | Deployment options | Score |
|---|---|---|---|---|---|---|---|---|---|
| 11 | What-If Tool | Yes | None | Yes | Yes | Yes | No | Hybrid | 7.2 |
| 1 | AIX360 | Yes | None | Yes | Yes | — | — | Self_hosted | 9.1 |
| 2 | Arize AI | Yes | $50/mo | Yes | — | Yes | — | — | 8.5 |
| 3 | Fiddler AI | Yes | $0/unit usage-based | — | — | — | — | — | 7.4 |
| 4 | InterpretML | Yes | None | Yes | — | Yes | — | Self_hosted | 7.4 |
| 5 | OmniXAI | Yes | None | Yes | Yes | Yes | — | Self_hosted | 7.4 |
| 6 | DALEX | Yes | None | Yes | Yes | Yes | — | Self_hosted | 7.4 |
| 7 | Responsible AI Toolbox | Yes | None | Yes | Yes | Yes | — | Self_hosted | 7.4 |
| 8 | Captum | Yes | None | Yes | Yes | — | — | Self_hosted | 7.4 |
| 9 | SHAP | Yes | None | Yes | No | Yes | — | Self_hosted | 7.4 |
| 10 | Alibi | — | — | Yes | Yes | No | No | Self_hosted | 7.3 |
| 12 | Shapash | Yes | None | Yes | No | No | Yes | Self_hosted | 7.1 |
| 13 | ExplainerDashboard | Yes | None | Yes | — | — | — | Self_hosted | 7.0 |
| 14 | DiCE | Yes | None | Yes | Yes | No | — | Self_hosted | 6.9 |
| 15 | Aequitas | Yes | None | No | No | Yes | Yes | Self_hosted | 6.8 |
| 16 | Fairlearn | Yes | None | — | No | Yes | — | Self_hosted | 6.7 |
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Last updated · How we research and update