Evidently vs Giskard
Both are eval frameworks tools. Here is how they actually differ on price, billing model and deployment.
Evidently
An open-source ML and LLM evaluation framework with 100+ metrics spanning tabular data through to GenAI. Notably, release 0.7.17 moved previously closed functionality into open source - the opposite of the usual direction.
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.
| Evidently | Giskard | |
|---|---|---|
| Category | Eval Frameworks | Eval Frameworks |
| Our rating | 4/5 | 4/5 |
| Starting price | Not published | 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 evaluating classical ML and LLM systems together, especially where data drift and data quality matter as much as output quality, and who want CI-integrated declarative testing. | 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 Evidently
Evidently is the broadest evaluation framework in this category and the only one that treats data quality, classical ML performance and LLM output as one continuous problem. That breadth is the reason to choose it. A team running recommender systems and a RAG assistant gets drift detection, ranking metrics and hallucination checks from one library with 100+ built-in metrics, where a purely LLM-native tool would cover a third of the surface. The design is also good - any Report becomes a Test Suite by adding pass/fail conditions, which makes CI gating a natural extension rather than a separate product. And release 0.7.17 moved previously closed functionality into open source, including the service for storing run logs and LLM-judge workflows, which is the opposite of the direction open-core vendors normally travel and deserves credit. The clear weakness is agents. There is no span-level evaluation for scoring individual steps like tool calls, no graph visualisation of execution paths, and agent evaluation needs custom work. If agents are your primary workload, look elsewhere.
Full Evidently 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 Evidently and Giskard?
Evidently: Teams evaluating classical ML and LLM systems together, especially where data drift and data quality matter as much as output quality, and who want CI-integrated declarative testing. 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, Evidently or Giskard?
It depends entirely on your workload shape, because they meter differently - Evidently bills on no usage metering and Giskard bills on no usage metering. Published starting prices are Not published 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 Evidently or Giskard?
Evidently: Yes, free. Giskard: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.