Key features
- 14+ metrics: APAC faithfulness, contextual recall, G-Eval, hallucination, toxicity
- Regression testing: APAC CI/CD quality gates blocking below-threshold LLM deployments
- Dataset management: APAC test case versioning and collaborative annotation
- DeepEval cloud: managed APAC evaluation infrastructure without self-hosting
- Dashboard: APAC metric trends and evaluation history across model versions
- Production monitoring: APAC live traffic evaluation sampling for ongoing quality tracking
Best for
- APAC AI engineering teams that need managed evaluation infrastructure with CI/CD quality gates — particularly APAC teams already using DeepEval locally who want collaborative dashboards, managed dataset storage, and automated regression testing without self-hosting evaluation backends.
Limitations to know
- ! Cloud dependency — APAC data sovereignty teams may prefer self-hosted DeepEval
- ! LLM-as-judge evaluation costs accumulate for APAC high-volume evaluation runs
- ! Dataset management limited on free tier — APAC teams need paid tier for large test suites
About Confident AI
Confident AI is the cloud platform built on top of DeepEval, the open-source LLM evaluation library — providing APAC teams with managed infrastructure for running DeepEval's 14+ evaluation metrics at scale, storing APAC test datasets, tracking evaluation results over time, and integrating quality gates into APAC CI/CD pipelines. APAC teams using DeepEval locally use Confident AI to share results, manage test datasets, and monitor APAC production LLM quality without self-hosting evaluation infrastructure.
Confident AI's evaluation metrics library covers the full APAC LLM quality surface: answer correctness, faithfulness (groundedness), contextual recall and precision for RAG, hallucination detection, toxicity, bias, G-Eval (custom criteria using LLM-as-judge), and summarization quality. APAC teams configure evaluation metric suites for their specific APAC use case — a financial services chatbot uses faithfulness + hallucination, a document QA system uses contextual recall + precision.
Confident AI's regression testing framework tracks APAC evaluation metric scores across LLM application versions — APAC teams define acceptable metric thresholds (e.g., faithfulness ≥ 0.85) and Confident AI blocks CI/CD promotion when a new APAC version drops below threshold on the APAC test dataset. This prevents shipping APAC LLM changes that degrade answer quality without explicit APAC team awareness.
Confident AI's dataset management stores and versions APAC test datasets in the cloud — APAC teams accumulate golden test cases from production APAC interactions (user queries + expected answers), upload them to Confident AI, and run regression evaluation on each new APAC model version against the same dataset. Collaborative APAC dataset annotation allows multiple team members to contribute quality labels for APAC production examples.
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