Confident AI
A dashboard that tells you whether your AI feature is actually getting worse or better over time, built on top of a popular open-source testing library instead of asking you to guess from user complaints.
🔗 Visit Confident AI
Description
Shipping an AI feature is easy to get wrong in a way traditional software isn't: the same prompt can produce a great answer one day and a subtly wrong one the next, and there's no compiler error to catch it. Confident AI is a platform for catching that — it runs structured tests and live-traffic checks against your AI application and shows you, in a dashboard, whether quality is holding up, the same way a testing/monitoring tool would for a normal web app, except tuned for the specific ways LLMs fail (hallucination, irrelevance, unsafe output).
Confident AI is the commercial platform built around DeepEval, a widely-used open-source LLM evaluation framework (17,000+ GitHub stars). It provides testing, monitoring and evaluation for LLM applications, letting both engineers and non-technical domain experts review production traces without needing to write code. Pricing runs Free (2 seats, 5 test runs/week, 1GB-month of traces), Starter at $200/month (unlimited seats, 5 projects, 5GB-month traces), Team at $2,000/month (unlimited seats/projects, 75GB-month traces), and custom Enterprise pricing with on-prem deployment and HIPAA support. The company reports serving 500+ AI companies including Panasonic, Samsung and Epic Games, raised on a $2.2M seed round.
💬 Our review
The short version: Confident AI's biggest asset is that it's built on DeepEval, an evaluation library with real, verifiable open-source traction (17k stars) rather than a from-scratch proprietary black box — that's a meaningfully lower-risk foundation than most LLM-eval startups can claim.
The honest gap is against Braintrust and Langfuse, both more established players in this exact space, and the jump from Free to Starter is steep ($0 to $200/month with no middle ground), which will sting smaller teams that outgrow the free tier's 5 test runs/week. Its headline customer names (Panasonic, Samsung, Epic Games) are self-reported without public case studies to verify scope of usage. For a team that's already using or considering DeepEval for testing, staying in the same ecosystem for production monitoring makes sense; for a team evaluating from scratch, it's worth comparing directly against Langfuse (MIT-licensed, closer feature-for-feature match) before committing to the $200/month tier.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Free : 2 sièges, 1 projet, 5 test runs/semaine, 1 GB-mois de traces. Starter $200/mois (sièges illimités, 5 projets, 5 GB-mois). Team $2000/mois (illimité, 75 GB-mois). Enterprise sur devis (on-prem, HIPAA).
Pros
Construit sur DeepEval, librairie open source avec 17 000+ stars vérifiables
Permet aux experts non-techniques de reviewer les traces sans coder
Tarif Free généreux pour démarrer (5 test runs/semaine)
Cons
Saut tarifaire abrupt de Free ($0) à Starter ($200/mois), sans palier intermédiaire
Marché déjà occupé par Braintrust et Langfuse, plus établis
Clients phares auto-rapportés sans étude de cas publique détaillée