Monte Carlo
Data and AI observability with automated anomaly detection
Monte Carlo is profiled here as a Observability tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Monte Carlo is a data observability platform founded in 2019 by Barr Moses and Lior Gavish. It connects to warehouses, lakes, transformation tools, and BI layers, learns normal behavior with machine learning monitors, and alerts when freshness, volume, schema, or quality drifts from baseline. Lineage ties every incident to the dashboards and AI applications it affects, which shortens root cause analysis from days to minutes. Deployment starts with read-only connections, so monitoring begins without moving data out of the customer environment.
Key Capabilities:
Automated anomaly detection for freshness, volume, and schema
End-to-end column-level lineage across the data stack
Incident management with root cause analysis workflows
Integrations for Snowflake, Databricks, BigQuery, dbt, Airflow, and BI tools
Observability extending to AI and LLM data pipelines
Custom monitors as code through API and SDK