LangSmith vs. Monte Carlo
A side-by-side comparison based on G2 review data.
| Metric | LangSmith | Monte Carlo |
|---|---|---|
| Overall rating | 4.4 | 4.3 |
| Review count | 78 | 567 |
| Ease of use | 8.9 | 8.5 |
| Ease of setup | 8.9 | 8.5 |
| Quality of support | 8.7 | 9.1 |
| Meets requirements | 9.1 | 8.6 |
| NPS | 65.384615 | 55.417407 |
| Small Business | 62.8% | 4.9% |
| Mid-Market | 25.6% | 42.2% |
| Enterprise | 11.5% | 52.9% |
| Total page views | 828 | 1,928 |
| Total LLM citations | 700 | 9,537 |
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.
Monte Carlo
Monte Carlo is the agent trust platform, trusted by Nasdaq, Cisco, PepsiCo, and hundreds of enterprise organizations worldwide. Founded in 2019 and backed by leading investors, Monte Carlo pioneered data observability and has expanded into the full AI reliability stack. We're consistently ranked #1 in data observability on G2 — and we're built for what comes next. As enterprises scale from dozens to thousands of AI agents across mission-critical use cases, Monte Carlo monitors, troubleshoots, and improves both those agents and the underlying data powering them.
- Ease of Use
- Alerts
- Monitoring
- Alerting System
- Data Quality
- Features