tools
Monte Carlo Data Observability Platform
Monte Carlo monitors data, ML models, and AI agents to detect quality issues, trace causes, and improve reliability.
Monte Carlo is a web platform for monitoring data quality across warehouses, lakes, databases, BI systems, pipelines, ML models, and AI agents. It detects freshness, volume, schema, and other issues, then provides alerting, lineage, impact analysis, root-cause analysis, and performance insights.
Data engineers, analysts, ML teams, governance teams, and data leaders use it to monitor tables, data products, model metrics, and agent workflows. Pricing is usage-based through credits or monitors, and the public pricing page does not display credit or plan prices.
Features
- Automated monitoring for freshness, volume, schema, and data quality issues
- Field-level lineage with upstream and downstream impact analysis
- Root-cause analysis correlating data, system, and code changes
- Automatic data profiling for field types, null rates, and cardinality
- Codeless, SQL, YAML, programmatic, and AI-assisted monitor creation
- Monitoring for structured and unstructured data
- ML metric monitors for RMSE, MAE, MAPE, R-squared, mean error, and accuracy
- API access with plan-specific daily call limits
Use cases
- Detect broken pipelines before they affect dashboards or AI systems
- Trace data incidents from source tables to downstream products
- Monitor ML model prediction quality and performance degradation
- Profile tables and deploy monitoring coverage without writing SQL
- Route alerts and coordinate incident investigation across data teams
- Monitor the data used by enterprise AI agents and applications