Confident AI
all-in-one LLM evaluation platform
Confident AI launched on July 29, 2024 with 86 votes, #814 of 5,184 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
What it does
Confident AI is the all-in-one LLM evaluation platform. It offers 14+ metrics to run LLM experiments, manage datasets, monitoring & integrates human feedback to automatically improve LLM apps. It works with DeepEval, our open framework & supports any use case.
Confident AI is the AI quality platform for enterprise teams to standardize AI evals and observability across the org — one consistent bar for how every team measures and monitors their AI.
Standardize how different teams turn live traces into test cases, validate with evals, and catch vulnerabilities before they ship. Align every team to the same evals and quality bar — no matter who ships the release. “We hit a point where every AI team was building their own eval stack. That’s fine for one product. With five, ten, fifteen AI initiatives across the portfolio, it’s never going to live up to our high standards of AI governance.” One platform that gives engineers, product owners, and QA teams a shared source of truth. Improving your score from 670 to 700 is achievable. A few strategies to start with: “Before Confident AI, a single improvement cycle took 10 days — I'd create a…from confident-ai.com
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I implemented evals metrics for LLMs that runs locally on your machine2023 · github.com · ▲22The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
TensorZero2025 · github.com · ▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…
DeepEval – Evaluation and Unit Testing for LLMs2023 · github.com · ▲18The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
Deepchecks LLM Evaluation2023 · deepchecks.com · ▲205Validate, monitor, and safeguard LLM-based apps
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