LangSmith vs. Arize Phoenix
A side-by-side comparison based on G2 review data.
| Metric | LangSmith | Arize Phoenix |
|---|---|---|
| Overall rating | 4.4 | 4.5 |
| Review count | 78 | 35 |
| Ease of use | 8.9 | 9.0 |
| Ease of setup | 8.9 | 8.8 |
| Quality of support | 8.7 | 9.0 |
| Meets requirements | 9.1 | 9.0 |
| NPS | 65.384615 | 62.857143 |
| Small Business | 62.8% | 55.9% |
| Mid-Market | 25.6% | 38.2% |
| Enterprise | 11.5% | 5.9% |
| Total page views | 828 | 243 |
| Total LLM citations | 700 | 104 |
LangSmith
LangSmith Observability gives you complete visibility into agent behavior. Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.
Arize Phoenix
Phoenix helps you understand and improve AI applications by giving you a workflow for debugging and iteration. You can send detailed logging information, known as traces, from your app to see exactly what happened during a run, score outputs using evaluation tests to identify failures and regressions, iterate on your prompts using real production examples, and optimize your app with experiments that compare changes on the same inputs. Together, these tools help you move from inspecting individual runs to improving quality with evidence.