Used For

• +**Your CEO's dashboard shows last week's numbers and you find out in the board meeting** → Get alerts at 7:30 AM when data will miss its 8 AM deadline, fix it before anyone notices
• +**Snowflake bill jumped 400% and nobody knows why** → Pinpoint which queries are burning cash and when they started going rogue
• +**Pipeline breaks and you spend 3 hours tracing through 12 connected tables** → Field-level lineage shows exactly which upstream change broke your downstream reports
• +Automatically detects schema changes and data volume drops - no writing custom monitoring scripts
• +AI agent analyzes incidents and tells you it's a queuing issue, not a code problem

Best For

• >Your data pipelines break every weekend and ruin someone's morning
• >You're spending 10+ hours a week fixing broken dashboards and angry Slack messages
• >Hit 50+ tables in Snowflake and manual monitoring became impossible

Not For

• -Teams under 20 people with fewer than 10 tables — you're paying enterprise prices for overkill
• -Anyone using legacy data warehouses like Oracle or Teradata — this only works with modern cloud platforms
• -Companies wanting real-time monitoring — this is built for scheduled batch jobs, not streaming data

Pairs With

• *Snowflake (the data warehouse it's actually monitoring - works with BigQuery and Redshift too)
• *dbt (Monte Carlo watches your transformations but doesn't replace the transformation logic)
• *Airflow (alerts when your DAGs miss SLAs or run slower than baseline)
• *Slack (where the 7:30 AM alerts land so you can fix things before the 8 AM deadline)
• *PagerDuty (for when data issues are serious enough to wake someone up)
• *Looker (shows which dashboards break when upstream data has problems)

The Catch

• !No public pricing means custom enterprise quotes that start around $10k+ annually
• !Setup is 15 minutes for basics, but defining custom business rules takes 2-4 hours per rule set
• !Customers with 30,000+ tables report performance issues that need support optimization

Bottom Line

Catches data fires before they burn down your quarterly review.