

Collibra and Monte Carlo are both Data Observability tools. Compare features, pricing, and ratings below to find the best fit for your team.
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Monte Carlo's ML-based freshness and volume anomaly detection learns baseline patterns automatically, alerting on incidents like a table that stopped updating 6 hours before the business noticed missing dashboard data.
Monte Carlo's lineage graph traces an anomalous dashboard metric back through dbt models, Fivetran pipelines, and source tables in under 2 minutes, cutting root cause analysis from hours to minutes.
Monte Carlo routes anomaly alerts to the Slack channel of the table owner based on data catalog metadata, ensuring incidents land with the right engineer rather than a generic alerts channel.
Best for: Small team, getting started
Best for: Scaling company, multiple domains
Best for: n/a (contact sales)
Best for: n/a (contact sales)
6 differences found across 15 standardized features
Evaluative strengths and weaknesses: not feature lists
Collibra removed the "Enterprise" plan
Plan removed · Jun 3, 2026
Collibra added a new "Ultimate" plan (Custom pricing)
Plan added · Jun 3, 2026
Collibra added a new "Premier" plan (Custom pricing)
Plan added · Jun 3, 2026
Collibra added a new "Standard" plan (Custom pricing)
Plan added · Jun 3, 2026
Monte Carlo removed the "Pro" plan
Plan removed · May 30, 2026
Monte Carlo added a new "Business Critical" plan (Custom pricing)
Plan added · May 30, 2026
Monte Carlo added a new "Scale" plan (Custom pricing)
Plan added · May 30, 2026
Monte Carlo added a new "Start" plan (Custom pricing)
Plan added · May 30, 2026
Monte Carlo added a new "Pro" plan
Plan added · May 21, 2026