Confident AI (DeepEval) vs Giskard
Both are eval frameworks tools. Here is how they actually differ on price, billing model and deployment.
Confident AI (DeepEval)
The pytest for LLM apps - write test cases, run "deepeval test run" in CI. The OSS framework is Apache-2.0 and free; the Confident AI cloud has a steep pricing cliff from $200/mo to $2,000/mo.
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 (DeepEval) | Giskard | |
|---|---|---|
| Category | Eval Frameworks | Eval Frameworks |
| Our rating | 4/5 | 4/5 |
| Starting price | $200/mo | Not published (Hub) |
| Billing meter | No usage metering | No usage metering |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | Yes, free |
| Best for | Python teams who want pytest-style LLM evals in CI/CD and can either live in the OSS framework or absorb the cloud's pricing steps | 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 (DeepEval)
If your team writes Python and thinks in tests, DeepEval is the most natural eval framework there is - it really does feel like pytest for LLM apps, and the OSS core is free under Apache-2.0. The friction is the cloud. The Free tier is stingy at 5 test runs a week, and the jump from $200/mo Starter to $2,000/mo Team is a real 10x cliff. Watch the API bills too - almost every metric is LLM-as-judge.
Full Confident AI (DeepEval) 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 (DeepEval) and Giskard?
Confident AI (DeepEval): Python teams who want pytest-style LLM evals in CI/CD and can either live in the OSS framework or absorb the cloud's pricing steps 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 (DeepEval) or Giskard?
It depends entirely on your workload shape, because they meter differently - Confident AI (DeepEval) bills on no usage metering and Giskard bills on no usage metering. Published starting prices are $200/mo 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 (DeepEval) or Giskard?
Confident AI (DeepEval): Yes, free. Giskard: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.