Confident AI

LLM evaluation platform built on DeepEval for testing, monitoring and improving AI applications.

4.6 (5)

리뷰어 Daniel Nikulshyn·업데이트됨 2026년 5월

개요

Confident AI is an evaluation and observability platform for teams building large language model applications. Powered by the open-source DeepEval framework, it provides a unified workspace to run benchmarks, regression tests and quality checks across prompts, models and retrieval pipelines. The platform helps engineers catch hallucinations, prompt regressions and retrieval failures before shipping, while offering production monitoring to track real user interactions. Teams can centralize datasets, share test results and iterate on prompts with measurable feedback rather than guesswork. It is aimed at developers, ML engineers and QA teams who want a structured, metrics-driven approach to LLM quality assurance rather than ad-hoc manual review.

주요 기능

DeepEval-powered evaluation metrics
Regression testing for prompts and models
RAG and retrieval evaluation
Production tracing and monitoring
Dataset and test case management
Team collaboration on evaluation results

장단점

장점

Built on the widely used DeepEval open-source library
Covers both pre-deployment testing and production monitoring
Centralized dataset and prompt management
Quantitative metrics for hallucination, relevance and more

단점

Primarily aimed at technical users familiar with LLM evaluation
Learning curve to design meaningful test cases
Value depends on integrating into existing dev workflows

리뷰

4.6

5개 평가의 평균.

리뷰를 작성하려면 로그인하세요.

Sanjay Gupta

Compared a few options

Evaluated this against two competitors. Where it wins: team collaboration on evaluation results and covers both pre-deployment testing and production monitoring. Where it lags: value depends on integrating into existing dev workflows. On balance the feature set — especially deepEval-powered evaluation metrics — justifies the 4 stars for our use case.

Frank Müller

Years in this space

I've evaluated a lot of these over the years. What stands out here is rAG and retrieval evaluation — handled better than most — and built on the widely used DeepEval open-source library. Worth the time if this is your use case.

Grace Okafor

Does the job

Pretty happy overall. Dataset and test case management just works and quantitative metrics for hallucination, relevance and more. Value depends on integrating into existing dev workflows can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Tariq Aziz

Compared a few options

Evaluated this against two competitors. Where it wins: production tracing and monitoring and quantitative metrics for hallucination, relevance and more. Where it lags: primarily aimed at technical users familiar with LLM evaluation. On balance the feature set — especially dataset and test case management — justifies the 5 stars for our use case.

Aaliyah Johnson

Compared a few options

Evaluated this against two competitors. Where it wins: production tracing and monitoring and covers both pre-deployment testing and production monitoring. On balance the feature set — especially team collaboration on evaluation results — justifies the 5 stars for our use case.

Q&A

아직 질문이 없습니다 — 첫 번째 질문을 해보세요.

질문하기

Observability 대안

AI2AI project

Observability

Watch two AI agents converse with each other in real time

4.5 (4)

Free

Weave

Observability

A no-code AI workflow builder that enables businesses to automate operations by integrating multiple large language models (LLMs) and connecting prompts seam...

4.8 (5)

Free

Temperstack

Observability

AI-driven reliability platform that automates monitoring, alerting, and incident management across observability stacks.

4.3 (4)

Free

Arize AI

Observability

An AI observability and LLM evaluation platform that assists AI developers and data scientists in monitoring, troubleshooting, and enhancing the performance...

4.3 (6)

Freemium