Inspect AI vs Giskard
Both are eval frameworks tools. Here is how they actually differ on price, billing model and deployment.
Inspect AI
The UK AI Security Institute's MIT-licensed eval framework, built for reproducibility rather than dashboards. Adopted by Anthropic, DeepMind and xAI - it is the closest thing this category has to a research-grade standard.
Giskard
Apache-2.0 testing and red-teaming library for LLM agents, strongest on adversarial security testing rather than quality metrics. The v3 rewrite requires Python 3.12+, which quietly rules it out for a lot of infrastructure.
| Inspect AI | Giskard | |
|---|---|---|
| Category | Eval Frameworks | Eval Frameworks |
| Our rating | 5/5 | 4/5 |
| Starting price | $0 (open source) | Not published (Hub) |
| Billing meter | No usage metering | No usage metering |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | Yes, free |
| Best for | Teams doing serious, reproducible model evaluation - safety testing, capability benchmarking, agent evaluation, or anything where the result has to withstand scrutiny. Also the right choice for anyone publishing evaluation results. | Teams that need adversarial testing and red teaming for LLM agents, especially in security-conscious or regulated settings, and who are on Python 3.12 or later. |
Our verdict on Inspect AI
Inspect AI is the most rigorous evaluation framework in this category and it is not particularly close. Built by the UK AI Security Institute, an organisation that runs thousands of evaluations on frontier models, its design goal was reproducibility - and it shows in choices nothing else makes, like shipping statistical bootstrap and confidence intervals rather than bare point scores, and logging complete transcripts of every sample. Sandboxed execution with Docker built in means you can safely evaluate models that write and run code. Agent and multi-turn workflows are first-class rather than retrofitted. It has been adopted by Anthropic, DeepMind and xAI, which is about as strong a signal as exists, since those are the organisations running the largest evaluation workloads anywhere. The trade-off is that it is a research instrument, not a product tool. It is Python-only, code-first, has no hosted service or dashboards, and does not do production observability at all. If you want to know whether your RAG chatbot is any good this week, use Ragas. If you need a number somebody will challenge, use this.
Full Inspect AI review →Our verdict on Giskard
Giskard occupies a different niche from most of this category and does it well. Where Ragas measures RAG quality and DeepEval gates CI, Giskard attacks your system - automated red teaming and adversarial vulnerability scanning against LLM agents, including black-box systems you did not build. That is a genuinely underserved capability, and it is Apache 2.0. Two things will decide whether you can use it. The v3 rewrite requires Python 3.12 or later, which quietly disqualifies any infrastructure pinned to 3.10 or 3.11 - a constraint we have not seen flagged in a single comparison, and one that will surface after you have already committed. And LLM-as-judge checks may require external API calls, so air-gapped environments need to check carefully what leaves the network. The v3 rewrite is also still maturing, with some v2 features not fully ported. Treat it as a security testing tool that complements a quality-focused framework, not as a replacement for one.
Full Giskard review →Frequently Asked Questions
What is the main difference between Inspect AI and Giskard?
Inspect AI: Teams doing serious, reproducible model evaluation - safety testing, capability benchmarking, agent evaluation, or anything where the result has to withstand scrutiny. Also the right choice for anyone publishing evaluation results. Giskard: Teams that need adversarial testing and red teaming for LLM agents, especially in security-conscious or regulated settings, and who are on Python 3.12 or later. Both sit in Eval Frameworks, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, Inspect AI or Giskard?
It depends entirely on your workload shape, because they meter differently - Inspect AI bills on no usage metering and Giskard bills on no usage metering. Published starting prices are $0 (open source) and Not published (Hub) respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.
Can I self-host Inspect AI or Giskard?
Inspect AI: Yes, free. Giskard: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.