AI Recommendation Standings / archive
LLM observability and evals: October 2026
The final October 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.
AI share of voice · October 2026
560 sampled answers · 10 models
- 1LangSmith▲ 0.50.5%Claude Sonnet 4.6 · Gemini 3.6 Flash—
- 2DeepEval▲ 0.20.2%Claude Sonnet 4.6—
- 3Helicone▲ 0.20.2%Claude Sonnet 4.6—
- 4Arize Phoenix±00%not named this month—
- 5Braintrust±00%not named this month—
- 6Langfuse±00%not named this month· quiet
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.
Quotable · copy freely
Averaged equally across 10 AI models, LangSmith is named in 0.5% of answers about llm observability and evals (2 of 10 name it).
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; 560 answers across 5 probe runs fed this month.