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. Our platform covers the full trust stack — from the data pipelines feeding agents, to the context they retrieve, the decisions they make, and the outputs they produce — across four trust dimensions: context quality, performance, behavior, and outputs. Only Monte Carlo closes the full trust loop across both data and AI, and we meet enterprises wherever they are on the spectrum from human-guided oversight to fully autonomous operations. With 100+ integrations across Snowflake, Databricks, and the rest of your stack, you get full coverage without ripping anything out. Traditional monitoring tools stop at the pipeline or cover only one dimension of reliability — leaving teams to manually investigate, diagnose, and fix failures across disconnected tools. Monte Carlo closes that gap. Teams using Monte Carlo dramatically reduce time to detect and resolve data and AI incidents, scale monitoring coverage without scaling headcount, and build the internal trust that turns AI investments into real business outcomes. If your organization is serious enough about AI to put it in front of customers, executives, and critical decisions — Monte Carlo is the foundation it needs.
Average Rating: 4.3/5.0
Total Reviews: 544
How Do G2 Users Rate Monte Carlo?
- Quality of Support: 9.0/10 (Category avg: 8.8/10)
- Automation: 7.5/10 (Category avg: 8.7/10)
- Identification: 8.1/10 (Category avg: 8.9/10)
- Preventative Cleaning: 6.1/10 (Category avg: 8.5/10)
Who Is the Company Behind Monte Carlo?
- Seller: Monte Carlo
- Company Website:
- Year Founded: 2019
- HQ Location: San Francisco, US
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Twitter: @montecarlo_ai
1,576 Twitter followers -
LinkedIn® Page: www.linkedin.com
550 employees on LinkedIn®
Who Uses This Product?
- Who Uses This: Data Engineer, Senior Data Engineer
- Top Industries: Financial Services, Computer Software
- Company Size: 50% Large, 41% Medium
What Do G2 Reviewers Say About Monte Carlo?
AI-generated summary from verified user reviews
Pros
- Users value the intuitive interface of Monte Carlo, finding it easy to navigate and utilize effectively.
- Users appreciate the custom alerts and integration with Teams, enhancing data monitoring and stakeholder communication efficiently.
- Users value the effective monitoring of Monte Carlo, catching data issues early and enhancing stakeholder communication.
- Users value the custom alerting features in Monte Carlo for efficiently monitoring and notifying stakeholders about data issues.
- Users value the ease of setting up alerts and anomaly detection in Monte Carlo for monitoring data quality.
Cons
- Users find the lack of manual threshold settings for alerts limiting, impacting customization for their specific needs.
- Users experience alert overload due to noisy initial settings, prompting the need for sensitivity adjustments and muted alerts.
- Users find the inefficient alert system problematic, with issues in notification messages and usability improvements needed.
- Users find the UX improvement necessary due to slow performance and disorganized features leading to confusion.
- Users find limited functionality in Monte Carlo, especially regarding custom metrics and alert threshold settings.
What Are Recent G2 Reviews of Monte Carlo?
"Easy, Reliable Monitoring & Alerting with Customizable Incidents"
Rating: 4.5/5.0 stars
— Eduardo A.
"Catches data issues before they become business problems"
Rating: 5.0/5.0 stars
— Muhammad Imran H.

