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Giskard

Freemium

Open-source testing framework for ML and LLM models covering robustness, bias, and security.

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Pricing

Freemium

Type

Automation

Languages

Python

// VERDICT

Reach for Giskard when you want automated scanning of ML/LLM models for vulnerabilities and quality issues, plus a regression test suite. Skip it when you want config-driven prompt evals (PromptFoo) or a hosted eval+tracing platform.

Best for

Testing and vulnerability scanning for ML and LLM models - automatically probing for issues like hallucination, bias, prompt injection and robustness, with a test suite you can run in CI.

Avoid when

You want a pure eval framework for prompts, a hosted observability platform, or you're not testing models.

CI/CD fit

Python library · scan + test suites · CI gates

Languages

Python

Team fit

ML/LLM teams · QA scanning models for issues · Responsible-AI/safety teams

Setup

Medium

Maintenance

Low

Learning

Intermediate

Licence

Freemium

// BEST FOR

  • Automatically scanning models for vulnerabilities and quality issues
  • Detecting hallucination, bias, robustness and prompt-injection risks
  • Generating a regression test suite from scan findings
  • Testing both ML and LLM models
  • Open-source with a hosted option
  • Wiring model tests into CI

// AVOID WHEN

  • You want a pure prompt-eval tool (PromptFoo)
  • A hosted observability platform is the need
  • You're not testing ML/LLM models
  • No-code-only evaluation is required
  • You need only manual human review
  • Turnkey enterprise scale is essential

// QUICK START

pip install giskard
# wrap your model + dataset -> giskard.scan() -> generate a test suite
# run the suite in CI

// ALTERNATIVES TO CONSIDER

ToolChoose it when
DeepEvalYou want metric-based LLM evals as unit tests.
PromptFooYou want prompt testing and red-teaming via config.
TruLensYou want feedback-function eval with tracing.

// FEATURES

  • Automatic vulnerability scans for ML models
  • Test suite generation across robustness, fairness, and performance
  • LLM scanning for hallucinations, prompt injection, and harm
  • Drift detection between training and production data
  • Giskard Hub for collaboration and continuous testing

// PROS

  • Covers both classical ML and LLM testing in one tool
  • Automated red-teaming aligned with EU AI Act expectations
  • Self-hostable open-source core
  • Clear, structured reports geared toward governance

// CONS

  • Advanced collaboration features sit behind the paid Hub
  • Best-in-class scans need a substantial dataset to be meaningful
  • Smaller integration ecosystem than tracking-focused tools

// EXAMPLE QA WORKFLOW

  1. Install Giskard (pip)

  2. Wrap your model and dataset

  3. Run a scan to surface vulnerabilities/issues

  4. Generate a regression test suite from findings

  5. Run the suite in CI and gate

  6. Re-scan periodically as the model evolves