Confident AI vs Giskard
Both are eval frameworks tools. Here is how they actually differ on price, billing model and deployment.
Confident AI
The commercial cloud layer over DeepEval. It recently moved from per-seat to flat per-organisation pricing at $200 and $2,000 a month - a change most third-party reviews have not caught up with.
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.
| Confident AI | Giskard | |
|---|---|---|
| Category | Eval Frameworks | Eval Frameworks |
| Our rating | 4/5 | 4/5 |
| Starting price | $200/mo per org | Not published (Hub) |
| Billing meter | gb-month | No usage metering |
| Free plan | Yes | Yes |
| Free self-hosting | No or paid tier only | Yes, free |
| Best for | Teams already using DeepEval who need shared datasets, persistence, online evaluation and collaboration, and who are large enough that unlimited seats on a flat plan beats per-seat competitors. | 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 Confident AI
Confident AI is the managed layer over DeepEval, and the most useful thing to know about it right now is that its pricing changed and most of the internet has not noticed. It now bills flat per organisation - $200 a month for Starter with unlimited seats and 5 GB-months, $2,000 for Team with 75 GB-months - having previously used a per-seat model that third-party reviews still quote at figures like $19.99 or $49.99 per seat. If you are comparison shopping from review sites you are working from stale numbers. The new model is genuinely favourable for larger teams, because unlimited seats on a flat plan beats per-seat pricing badly once you have more than a handful of engineers, and traces are unlimited on every tier with billing on stored data instead. The underlying DeepEval framework is Apache 2.0 and free, so your evaluation logic stays portable, and a self-hosted option exists. The main structural criticism is the 10x gap between Starter and Team with nothing published in between.
Full Confident 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 Confident AI and Giskard?
Confident AI: Teams already using DeepEval who need shared datasets, persistence, online evaluation and collaboration, and who are large enough that unlimited seats on a flat plan beats per-seat competitors. 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, Confident AI or Giskard?
It depends entirely on your workload shape, because they meter differently - Confident AI bills on gb-month and Giskard bills on no usage metering. Published starting prices are $200/mo per org 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 Confident AI or Giskard?
Confident AI: No or paid tier only. Giskard: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.