# Confident AI: what AI models say (September 2026)

Source: Memetik Index, https://www.memetik.ai/vendors/confident-ai
Website: https://www.confident-ai.com

Confident AI was named in 34 of 50 AI answers across 1 category. Best position: LLM observability and evals, 68% answer share, ranked #5, named first in 12%.

## What Confident AI is

Confident AI is a platform for evaluating, monitoring, testing and governing AI applications. It is for engineers, product owners and QA teams building AI products, including teams in regulated industries.

The platform records LLM and tool calls, agent performance, latency, token use and cost. It monitors production traces for quality or latency regressions and sends alerts. Users can turn traces into evaluation datasets, categorise failures and edge cases, and test applications through HTTP and streaming endpoints. It also runs chat simulations and centralises red-teaming workflows to identify AI risks before release.

(From Confident AI's own website, read 2026-09-02.)

## By category

| Category | Rank | Answer share | Named first | Models |
|---|---|---|---|---|
| LLM observability and evals (https://www.memetik.ai/index/llm-observability) | #5 of 13 | 68% | 12% | GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |

## What the models said

> "Go with Confident AI if your primary challenge is evaluating agent quality and preventing regressions in CI/CD pipelines."
> — Gemini, LLM observability and evals

> "Choose Confident AI if “production evals” matters more than anything else and you want the platform to center quality measurement and regression detection."
> — Perplexity, LLM observability and evals

> "Choose Confident AI if your application is highly sensitive to hallucinations, correctness, or safety, and you want production monitoring built around automated, research-backed metrics."
> — Gemini 3.5 Flash, LLM observability and evals

> "Confident AI / DeepEval Evaluation-heavy platform emphasizing closing the loop between production and testing."
> — Claude, LLM observability and evals