Confident AI

Confident AI is a team workspace for AI quality: LLM evaluation, production observability, red teaming, and governance under one roof. It's aimed at organizations running several AI products at once, where engineering, product, QA, and subject-matter experts need one shared standard instead of each team building its own eval stack. The site's own framing is blunt — an in-house eval stack works for one product, not fifteen. The loop is trace, annotate, evaluate. Observability captures span trees with latency, token, and cost data, auto-curates production traces into eval datasets, and alerts when quality drops. Evaluation runs on 30+ single-turn and 15+ multi-turn DeepEval metrics — faithfulness, relevancy, hallucination, tool correctness — plus natural-language G-Eval criteria and deterministic code metrics. Non-engineers can fire regression runs at live HTTP and streaming endpoints and simulate thousands of multi-turn conversations without hand-prompting. Recent additions lean into production analytics and adversarial testing. Tool Failure Analytics ranks which tools fail and which agents pick the wrong one; Customer Monitoring groups usage, sentiment, and cost by account; AI Drift Detection versions agent configs and flags regressions; Category Correlation finds which classifier signals move together. Jev-as-a-Judge makes online evals cheap enough to run on 20–50% of traces. Red teaming covers the OWASP Top 10 for Agentic Applications with PII leakage, jailbreaking, and prompt-injection probes plus PDF risk reports. Pricing is freemium — Free Forever at $0, Starter at $200/mo, Team at $2,000/mo. Compared with point tools like Langfuse or LangSmith, you're paying for the eval depth, human annotation loop, and red teaming in a single place rather than a tracing-first product.