Monte Carlo pioneered “data observability”—applying infrastructure monitoring concepts to data quality with ML-powered anomaly detection. Pricing is enterprise-only (estimates $50,000-200,000+/year based on data volume). The platform monitors five pillars: freshness (is data up to date?), volume (expected row counts?), schema (unexpected changes?), distribution (values within norms?), and lineage (what’s affected?). ML models learn normal patterns automatically—no manual threshold configuration. Root cause analysis traces issues through lineage to identify source problems. The platform connects to warehouses (Snowflake, BigQuery, Databricks) with no data extraction—monitoring metadata and query logs. Field-level lineage tracks 30+ BI and transformation tools. Slack/PagerDuty integration enables alerting in existing incident workflows. Best suited for organizations with complex data stacks wanting proactive quality monitoring. Page should cover: five pillars of data observability, ML anomaly detection methodology, lineage capabilities, integration ecosystem, comparison with Soda and Bigeye, pricing expectations, and implementation requirements.