Monte Carlo
Data and AI observability platform
About
Founded in 2019 by Barr Moses and Lior Gavish, Monte Carlo is based in San Francisco and is credited with coining the term data observability. The platform monitors data warehouses, lakes, ETL pipelines, dashboards, and AI model inputs and outputs for freshness, volume, schema, distribution, and lineage anomalies, alerting data teams before downstream consumers are affected. In 2025 Monte Carlo became the first vendor to unify data and AI observability—including agent output monitoring—in a single platform. Customers span technology, financial services, and retail enterprises that run large-scale data and ML pipelines.
Products
-
Data and AI Observability Platform
— Monitors and traces data inputs and outputs to ensure trust and reliability across data and AI ecosystems.
Customer sectors: data + AI leaders, data engineers, data governance
-
Agent Observability
— Provides full visibility into agent context, performance, behavior, and outputs to monitor and troubleshoot AI agents.
Customer sectors: data engineers, AI/ML teams, operations
-
Troubleshooting Agent
— Helps diagnose and resolve issues with AI and data agents to maintain reliability and accuracy in production.
Customer sectors: devops engineers, data engineers, operations
-
Monitoring Agent
— Continuously monitors AI and data agents to detect anomalies and performance issues in the data ecosystem.
Customer sectors: data engineers, operations, mid-market ecommerce
-
Operations Agent
— Automates operational workflows to improve reliability and efficiency of data and AI agent processes.
Customer sectors: operations, data governance, data engineers
Market research & competitive positioning
Source-backed TAM / SAM / SOM sizing, displacement analysis, and an interactive peer positioning map for Monte Carlo — available to members.
Related Companies
Other startups in Data Management Platforms
