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LLM observability and evals: September 2026

The final September 2026 standings for “What should I use for LLM observability and evals?”. These numbers are fixed and can be cited by permalink. See the current standings.

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AI share of voice · September 2026

445 sampled answers · 9 models

  1. 1Arize Phoenix▼ 14.20%
    not named this month—
  2. 2Braintrust▼ 15.90%
    not named this month—
  3. 3DeepEval▼ 9.70%
    not named this month—
  4. 4Helicone▼ 19.60%
    not named this month—
  5. 5Langfuse▼ 22.80%
    not named this month· quiet
  6. 6LangSmith▼ 23.50%
    not named this month—

Biggest mover: LangSmith ▼ 23.5 pts vs the previous month

Share of voice weights each compared model equally: it is the average, across the models, of how often each one named the tool in its own answers, so a model probed more than another can't skew it. Model chips list which AI models named it this month; the pulse is the sentiment of that month's developer mentions across Hacker News, Reddit, GitHub, Stack Overflow, Bluesky and more.

The 3 prompts behind these standings
  • ›What should I use for LLM observability? (recommendation)
  • ›What are the best LangSmith alternatives? (alternatives)
  • ›Which tool is best for evaluating LLM outputs? (recommendation)

Each prompt is sampled several times per model per probe because answers vary run to run; 445 answers across 4 probe runs fed this month.