Monte Carlo
End-to-end data observability platform that uses machine learning to infer and learn your data.
Monte Carlo is the data reliability company. Their end-to-end data observability platform uses machine learning to infer and learn your data, automatically detecting data issues before they impact your business. The platform provides comprehensive monitoring, alerting, and root cause analysis for data pipelines.
Founded by former Uber and Facebook engineers, Monte Carlo focuses on preventing data downtime - periods of time when data is partial, erroneous, missing, or otherwise inaccurate. Their ML-powered approach means less manual configuration and faster time to value.
JetBlue Data Engineering
Airlines & Transportation
"Monte Carlo provides flight-ops observability, helping us maintain data reliability for critical operations."
Source: montecarlodata.com
Data Observability
End-to-end monitoring of data pipelines and quality
Anomaly Detection
ML-powered detection of data quality issues
Lineage Tracking
Automatic discovery and mapping of data lineage
Smart Alerting
Intelligent notifications with context and recommendations
- •ML-powered insights reduce false positives
- •Quick setup with minimal configuration
- •Excellent user interface and experience
- •Strong customer support and success team
- •Comprehensive data platform integrations
- •High cost, especially for smaller teams
- •Limited customization options
- •Primarily focused on modern cloud data stacks
- •Learning curve for advanced features
Enterprise
Contact Sales
Custom pricing based on data volume and features needed
- • Full data observability platform
- • ML-powered anomaly detection
- • Data lineage and impact analysis
- • 24/7 support and customer success
- • SSO and enterprise security
Pricing typically starts around $2,000-$5,000 per month depending on data volume and requirements.
Enterprise Teams
Large organizations with complex data infrastructure
Data-Driven Companies
Businesses where data downtime has high impact
Modern Data Stacks
Teams using Snowflake, BigQuery, Databricks, etc.
Time to Value
1-2 days for initial insights
Technical Requirements
Cloud data warehouse, read-only access
Implementation Complexity
Simple - mostly automated setup
Required Expertise
Basic SQL knowledge helpful
Onboarding Support
Dedicated customer success team
Learning Curve
Gentle - intuitive interface
Native Integrations
Snowflake, BigQuery, Redshift, Databricks, dbt
API Quality
REST API, GraphQL, comprehensive docs
Data Export/Import
CSV exports, API access to all data
Webhook Support
Slack, PagerDuty, email, custom webhooks
Pre-built Connectors
50+ data platform connectors
Custom Development
Limited - focus on out-of-box functionality
Implementation Cost
$5K-15K including professional services
Ongoing Maintenance
Low - fully managed service
Contract Terms
Annual contracts, volume discounts
ROI Timeline
2-4 months via prevented data incidents
vs Alternatives
Premium pricing but faster time-to-value