Best AI evaluation tools for production

LLM evaluations complement unit tests by assessing output quality, including usefulness, relevance, hallucinations, safety, and retrieval grounding, through LLM-as-judge methods, deterministic code checks, and human review. The comparison presents PostHog as a broad choice for connecting evaluation scores with product analytics, session replays, traces, feature flags, and releases; Braintrust for experiment tracking and pull-request feedback; Langfuse and Arize Phoenix for self-hosted tracing and evaluation; and DeepEval for pytest-style evaluation gates. Ragas is positioned for RAG retrieval metrics, TruLens for OpenTelemetry-based and agent-specific evaluation, and LangWatch for simulated multi-turn and voice-agent testing. Key selection criteria include CI/CD integration, production monitoring, self-hosting requirements, licensing, pricing, observability support, and whether teams need output scores tied to real user behavior rather than only traces or offline datasets.

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