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Monte Carlo

Monte Carlo

Data Infrastructure · www.montecarlodata.com

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Overview

Monte Carlo is a data observability platform that monitors warehouses, lakes, databases, BI assets, pipelines, and AI data flows for freshness, volume, schema, lineage, and quality issues. It helps data teams detect incidents, understand downstream impact, investigate root causes, and create a common reliability layer across the modern data stack.

Best for

  • Data-platform and analytics engineering teams
  • Organizations with many downstream data consumers
  • Teams operating warehouse, lake, and AI pipelines

Not a fit if

  • Small analytics stacks with manual checks
  • Teams without data ownership
  • Buyers seeking only infrastructure monitoring

Why it’s listed

  • Strong data-observability category fit
  • Covers both classic data and AI pipelines
  • Clear plan capability boundaries
  • Useful for organizations scaling data products

Pricing

Start

Custom pricing

Monitoring for warehouse, BI, ETL, incidents, lineage, and up to 1,000 tables.

  • Data quality
  • Lineage
  • Incident triage

Scale

Custom pricing

Expanded lake, database, GenAI, data-mesh, security, and automation coverage.

  • Lake monitoring
  • AI pipelines
  • Webhooks

Enterprise

Custom pricing

Enterprise workspaces, governance, audit, and broader database coverage.

  • Audit logs
  • SAP HANA
  • ServiceNow

Features

Data quality monitoringChecks freshness, volume, schema, and quality patterns.
LineageShows downstream impact of data incidents.
Incident triageHelps investigate and prioritize data failures.
Warehouse monitoringSupports major analytical warehouses.
Database monitoringCovers MySQL, PostgreSQL, and SQL Server on higher tiers.
AI pipeline observabilityMonitors Kafka and vector-data workflows.
Data-mesh governanceAdds domains and data-product context.
Response automationRoutes issues via webhooks and operations tools.

Integrations

SnowflakeBigQueryDatabricksRedshiftAmazon S3Google Cloud StorageMySQLPostgreSQLSQL ServerKafkaPineconedbtAirflowServiceNowSlackPagerDuty

Security & compliance

SOC 2ISO 27001GDPR

Pros & cons

Pros

  • Broad stack coverage
  • Lineage and root-cause workflows
  • AI pipeline monitoring
  • Strong enterprise governance

Cons

  • Quote-based pricing
  • Value grows with data-stack complexity
  • Requires metadata and access setup
  • Observability adds another operating layer

What the record shows

—/5
No reviews yet aggregated
G2 ↗Capterra ↗

Monte Carlo publishes Start, Scale, and Enterprise coverage with pricing by table; official materials describe monitoring for warehouses, lakes, databases, BI, ETL, AI pipelines, lineage, and incident response.

Summary and score aggregated from public review platforms. We link to original reviews rather than reproducing them — read the source before deciding.

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