Confident AI (DeepEval) vs Evidently
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.
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.
| Confident AI (DeepEval) | Evidently | |
|---|---|---|
| Category | Eval Frameworks | Eval Frameworks |
| Our rating | 4/5 | 4/5 |
| Starting price | $200/mo | Not published |
| 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 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. |
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 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 →Frequently Asked Questions
What is the main difference between Confident AI (DeepEval) and Evidently?
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 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. 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 Evidently?
It depends entirely on your workload shape, because they meter differently - Confident AI (DeepEval) bills on no usage metering and Evidently bills on no usage metering. Published starting prices are $200/mo and Not published 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 Evidently?
Confident AI (DeepEval): Yes, free. Evidently: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.