Captum

At a glance

• Editor score
  7.4 / 10
• Pricing
  Free plan
• Best for
  PyTorch teams needing detailed attribution analysis
• Free plan
  Yes
• Paid from
  None
• Feature attribution
  Yes
• Facts checked
  28 Sep 2026

• Where it wins

  • Offers feature, layer, neuron and concept-based attribution methods
  • Includes infidelity and sensitivity metrics for explanation quality
  • Adds influence analysis, robustness tools and LLM attribution classes

• Where it doesn't

  • Designed around PyTorch rather than a broad multi-framework ecosystem
  • Requires teams to work with a Python library, not a hosted service
  • Support channels are docs and community

Our verdict on Captum

Captum is an open-source Python library for interpreting PyTorch models. ML researchers, model developers and application engineers can use its attribution methods to examine feature, layer and neuron contributions, troubleshoot predictions, or communicate how a model produced an output. It is a library rather than a hosted service, so it suits teams that want to incorporate interpretability into their own Python workflows and manage deployment themselves.

Its scope goes beyond feature attribution. Methods include Integrated Gradients, Saliency, DeepLIFT, SHAP, LIME and perturbation techniques, alongside concept-based analysis with TCAV and influential-example methods such as TracIn. Infidelity and sensitivity metrics help assess explanation quality; counterfactual perturbations and adversarial-robustness tools support further investigation. Captum also includes attribution classes for large language models and visualization utilities. This breadth makes it useful when a team needs several complementary ways to inspect model behavior, rather than a single explanation method.

Captum is free and self-hosted, with integrations centered on PyTorch, torchvision and torchtext; large-language-model support uses Hugging Face-style interfaces. Documentation and community are its listed support channels. The PyTorch focus is a meaningful boundary: teams working primarily in another machine-learning framework may be better served by a framework-specific alternative. Choose Captum if your team works in the PyTorch ecosystem and wants detailed attribution analysis without a hosted service; look elsewhere if a managed product or broader framework coverage is central to your needs.

Your product? Our editors can test Captum in depth and publish a review, labelled as paid · from $99/mo →

Captum pricing

Captum fact sheet

Captum integrations

Captum lists 3 integrations on its own site.

• PyTorch
• torchvision
• torchtext

Alternatives to Captum

• AAIX360A broad, self-hosted Python toolkit for local, global, and time-series model explanations.9.1
• AArize AIProduction-focused explainability pairs feature attribution with fairness monitoring and model comparisons.8.5
• Fiddler AIA tracing and evaluation platform for teams debugging LLM and agent workflows.7.4

See all Captum alternatives →

Captum vs the competition

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Featured on iTechGuides

Captum is listed in our AI Model Explainability Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 9 Oct 2026 · How we research and update