Startups > Monte Carlo

Monte Carlo

Monte Carlo helps data teams prevent data downtime with AI-powered observability.
Series D $236M total Founded 2019 San Francisco, California 165 employees
Monte Carlo is an end-to-end Data + AI Observability Platform that monitors data warehouses, lakes, ETL pipelines, and BI tools to automatically detect anomalies, identify root causes, and alert teams to data issues before they impact business. The platform uses machine learning and baseline-driven anomaly detection to eliminate manual threshold-setting and validation rules. It serves data-driven enterprises like Nasdaq, Honeywell, and Roche who need real-time visibility into data health across their entire stack.
Problem solved
Data teams lack visibility into data quality issues across their stack, leading to unreliable analytics, broken dashboards, and slow incident response when data breaks.
Target customer
Enterprise and mid-market companies with complex data stacks (data warehouses, lakes, ETL, BI tools) that require real-time data quality monitoring and incident response.
Website LinkedIn Crunchbase
Founders
B
Barr Moses
CEO & Co-Founder
Former VP of Customer Operations at Gainsight and commander of intelligence data analyst unit in Israeli Air Force; Stanford BS in Mathematical and Computational Science, MBA from Stanford, BSc in Industrial Engineering from Tel Aviv University.
L
Lior Gavish
Co-Founder & CTO
I
Itay Bleier
Co-Founder & Head of Engineering
J
Jordan Van Horn
Co-Founder & COO
Funding history
Series A Unknown September 2020 Led by Accel, GGV Capital · Unknown
Series B $25M February 2021 Led by Unknown · Unknown
Series C $60M Unknown Led by ICONIQ Growth · Salesforce Ventures, Accel, GGV Capital, Redpoint Ventures
Series D $135M May 2022 Led by IVP · Accel, GGV Capital, Redpoint Ventures, ICONIQ Growth, Salesforce Ventures, GIC Singapore
Total raised: $236M
Industries
Enterprise Software Analytics Information Technology Software
Pricing
Consumption-based model tied to data assets monitored, data sources connected, and observability rule complexity. Start Tier: Pay per monitor (up to 1,000), 10 users, 10,000 API calls/day. Scale Tier: Enterprise coverage with unlimited users, multiple data sources, 100,000 API calls/day. Enterprise deals typically range $60,000–$120,000 annually. Custom pricing available.
Notable customers
Nasdaq, Honeywell, Roche, Intuit, Affirm, Fox, Vimeo, PagerDuty, Zalora, The Farmer's Dog, American Airlines, Texas Rangers
Integrations
AI coding agents (Claude Code, Cursor, VS Code via MCP Server), data warehouses (Snowflake, BigQuery, Redshift, Databricks, etc.), ETL tools, BI platforms, Salesforce Ventures ecosystem
Website
montecarlodata.com/
Competitors
New Relic
Broader infrastructure and application monitoring platform; Monte Carlo is more specialized and cost-effective for data-specific observability.
Datadog
General infrastructure monitoring with data capabilities; Monte Carlo offers deeper data quality and lineage features at lower cost for data teams.
Bigeye
Competitor in data quality space; Monte Carlo's anomaly detection engine and AI-driven approach provides automated baseline learning without manual rules.
Anomalo
Data quality tool; Monte Carlo provides more comprehensive end-to-end observability and enterprise features.
Ascend
Data observability competitor; Monte Carlo has broader customer base and more mature platform.
Great Expectations
Open-source data validation tool; Monte Carlo is commercial, fully-managed, and requires no custom rule writing.
Why this matters: Monte Carlo became the first data observability company to reach unicorn status ($1.6B valuation), reflecting the growing importance of data reliability as a business-critical function. The company's AI-driven approach to anomaly detection—learning baselines automatically without manual rules—represents a meaningful shift in how enterprises manage data quality at scale.
Best for: Enterprise organizations with large, complex data stacks who need automated, real-time visibility into data quality and want to reduce time spent on data incident detection and root cause analysis.
Use cases
Preventing Data Downtime in BI & Analytics
A retailer's BI dashboards show incorrect sales figures due to a broken ETL pipeline. Monte Carlo's anomaly detection catches the data quality issue immediately and alerts the data team before executives see bad data. Teams resolve the incident in minutes instead of hours or days.
Cross-Functional Data Incident Response
When a data issue occurs, Monte Carlo's lineage and impact assessment shows which downstream dashboards, reports, and ML models are affected. The platform notifies the relevant teams (analytics, product, finance) automatically so they can coordinate fixes faster.
Data Catalog & Governance at Scale
A large enterprise has hundreds of data tables across multiple warehouses. Monte Carlo's automated field-level lineage and data cataloging eliminates manual documentation, helping teams understand data accessibility, ownership, and health without maintaining spreadsheets.
Alternatives
Great Expectations Open-source and free but requires engineering resources to write validation rules; Monte Carlo is fully-managed with ML-driven automation.
Datadog Broader infrastructure monitoring platform with data capabilities; choose Datadog if you need unified infrastructure + data monitoring; Monte Carlo if data quality is your primary need.
Bigeye Similar data quality focus; choose Bigeye for rule-based validation or Monte Carlo for autonomous anomaly detection without manual threshold setting.
FAQ
What does Monte Carlo do? +
Monte Carlo is a Data + AI Observability Platform that monitors your entire data stack (data warehouses, lakes, ETL, BI tools) using machine learning to automatically detect anomalies, identify root causes, and notify teams. It eliminates the need to write manual validation rules or set complex thresholds—the platform learns your data baselines and alerts you to deviations that matter.
How much does Monte Carlo cost? +
Monte Carlo uses consumption-based pricing tied to the number of data assets monitored and data sources connected. Start Tier costs are pay-per-monitor with limited users and API calls. Scale Tier is for enterprise deployments. Enterprise annual contracts typically range from $60,000–$120,000 depending on data volume and complexity. Contact sales for custom pricing.
What are alternatives to Monte Carlo? +
Top alternatives include Great Expectations (open-source data validation), Datadog (broader infrastructure monitoring), Bigeye (rule-based data quality), Anomalo, and Ascend. Choose Great Expectations if you want self-managed and free; Datadog if you need unified infrastructure + data monitoring; Monte Carlo if you prioritize automated anomaly detection without manual rules.
Who uses Monte Carlo? +
Enterprise and mid-market companies with large, complex data stacks. Notable customers include Nasdaq, Honeywell, Roche, Intuit, Affirm, Vimeo, and PagerDuty. Any data-driven organization that relies on data warehouses, ETL pipelines, and BI tools can benefit from Monte Carlo's observability platform.
How does Monte Carlo compare to Great Expectations? +
Great Expectations is open-source and free but requires engineers to write and maintain validation rules. Monte Carlo is a fully-managed SaaS platform with ML-driven anomaly detection that requires no manual rule writing. Monte Carlo learns your data automatically, scales to enterprise environments, and includes incident response and lineage features Great Expectations lacks.
Does Monte Carlo integrate with my data stack? +
Yes, Monte Carlo integrates with all major data platforms including Snowflake, BigQuery, Redshift, Databricks, and others. It also integrates with popular ETL and BI tools, and recently added integration with AI coding agents like Claude Code and Cursor via MCP Server.
Tags
data observability data quality anomaly detection data reliability machine learning data catalog lineage incident response