Best LLM observability tools in 2026

The LLM observability category has split into three sub-categories. Prompt analytics (LangSmith, Helicone, PromptLayer, Phoenix/Arize) watches your application traffic — useful for debugging, prompt iteration, cost attribution. Evaluation frameworks (OpenAI Evals, DeepEval, Promptfoo, Inspect, lm-evaluation-harness, HELM) run scored benchmarks but are not continuous services. Model drift monitoring (ModelWatch) runs provider-side eval suites on a schedule and alerts on regression.

Pick by job-to-be-done. Debugging a misbehaving prompt? Helicone or LangSmith. Comparing prompt variants pre-prod? Promptfoo or DeepEval. Proving the model got worse this week? ModelWatch or a custom harness on lm-evaluation-harness + cron + Grafana. Teams running serious production AI typically run one of each layer — traffic observability for app-side issues plus model-drift monitoring for the provider-side surprises that traffic observability cannot see.