Data observability · Monte Carlo Data, Inc.

Monte Carlo

Data observability platform using ML-based anomaly detection to catch broken pipelines before they reach dashboards.

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Monte Carlo monitors freshness, volume, schema and distribution across warehouse and lake tables, applying machine-learning models to learn normal patterns and flag anomalies without every check being hand-written. It builds automated table and column-level lineage so an alert can be traced to its likely root cause and downstream blast radius, including which dashboards and models are affected. Incident management features route alerts to Slack or ticketing systems and track resolution over time, and "monitors as code" let teams define custom checks alongside their dbt or Airflow projects. Monte Carlo is a pure cloud SaaS product, billed on a credit and pay-per-monitor basis across four named tiers. It is most often adopted by mid-size to large data platform teams that need broad automated coverage across many tables rather than writing every rule manually.

At a glance

Vendor Monte Carlo Data, Inc.
Pricing model Quote only
Free tier No
Deployment Cloud
Open source No
Best for Enterprise data teams needing broad, automated monitoring across many warehouse tables.

Pricing

Credit-based, pay-per-monitor pricing across four named tiers; actual credit rates require a sales quote.

Plan Price Notes
Start Custom, pay per monitor up to 10 users, up to 1,000 monitors, 10,000 API calls/day
Scale Custom, pay per monitor unlimited users, 50,000 API calls/day
Enterprise Custom, pay per monitor unlimited users, 100,000 API calls/day
Business Critical Custom, pay per monitor maximum availability for mission-critical environments

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Automated data lineage and impact analysis
  • ML-based anomaly detection
  • Freshness, volume and schema monitoring
  • Incident management and alerting
  • Root cause analysis
  • Monitors-as-code
  • dbt and Airflow pipeline context integration

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Bigeye Data observability platform that auto-generates quality metrics and anomaly alerts across warehouses and lakes. Metaplane Lightweight data observability tool with a genuine free tier and per-table usage pricing, including Snowflake credit billing. Sifflet End-to-end data observability platform combining anomaly detection, lineage and governance controls for data and AI assets.

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Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. Diagnostic analytics Analytics focused on explaining why something happened by drilling into patterns, correlations, and root causes behind results. Root cause analysis (RCA) A structured process for tracing a problem back through its contributing factors to the underlying cause, not just its symptoms.

Related guides

How to choose a data observability tool Pick a data observability tool by how it generates coverage, where it needs to run relative to your data, and whether you're already living inside dbt.