Monte Carlo
Monte Carlo: Broad monitoring, lineage, and incident analysis for data and AI teams. Ranked #4 of 39 in Data Observability Tools by our editors (8.9/10); pricing: Pricing on request; best for teams needing broad, automated data observability.
At a glance
- Editor score8.9 / 10
- PricingPricing on request
- Best forTeams needing broad, automated data observability
- Free planNo
- Facts checked4 Oct 2026
Where it wins
- Automates freshness, volume, schema, and data quality monitoring
- Combines field-level lineage with downstream impact and root-cause analysis
- Supports structured and unstructured data plus SQL and YAML monitor creation
Where it doesn't
- Pricing is usage-based and requires contacting sales
- Start limits teams to 10 users and charges per monitor
- Advanced security and enterprise capabilities are reserved for higher tiers
Our verdict on Monte Carlo
Monte Carlo brings monitoring, lineage, alerting, and investigation together for data and AI teams working across pipelines and related assets. Automated checks cover freshness, volume, and schema, while AI-powered rules and templates support data quality monitoring. Automatic profiling tracks field types, null rates, and cardinality. It is suited to teams that want broad observability rather than a tool focused only on freshness or pipeline status.
The platform connects with data warehouses, lakehouses, databases, orchestration systems, and BI tools, including Snowflake, Databricks, BigQuery, PostgreSQL, Airflow, dbt, Looker, and Tableau. Monitors can be created through the interface, SQL, YAML-based CI/CD configuration, or programmatically. Field-level lineage and downstream impact analysis help connect a detected issue to affected assets; root-cause analysis correlates data, system, and code changes. Alert routing includes destinations such as Slack, PagerDuty, ServiceNow, and webhooks. That breadth makes Monte Carlo a fit for teams with varied data infrastructure and incident workflows.
According to the vendor's pricing page, plans use per-monitor pricing, with prices available through sales. Start includes up to 10 users and 10,000 API calls per day. Scale adds advanced security features such as SSO, SCIM, self-hosted storage, PII filtering, and audit logging, as well as exports and webhooks. Enterprise adds multi-workspace support and cost attribution; Business Critical adds a dedicated instance and disaster recovery with regional rollover. Choose Monte Carlo if monitoring breadth, lineage, and investigation matter across a growing data estate. Teams seeking a free plan or published prices may prefer to evaluate alternatives; larger organizations can consider the higher tiers for their additional controls and deployment options.
Your product? Our editors can test Monte Carlo in depth and publish a review, labelled as paid · from $149/mo →
Monte Carlo pricing
All 4 Monte Carlo plans and prices →
Monte Carlo fact sheet
| Free plan | No |
|---|---|
| Paid from | Not verified |
| Pipeline monitoring | Not verified |
| Freshness monitoring | Yes |
| Schema change detection | Not verified |
| Anomaly detection | Not verified |
| Data lineage | Not verified |
| Deployment | Not verified |
| Deployment | Cloud, Self-hosted |
| Platforms | Web |
| Compliance & security | SSO/SAML |
| Support | Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 50+ integrations: Snowflake, Databricks, BigQuery, Amazon Redshift, PostgreSQL, MySQL … See all → |
| Pricing | Pricing on request (source) |
| Website | montecarlo.ai |
| Facts checked | 4 Oct 2026 |
Monte Carlo integrations
Monte Carlo lists 50+ integrations on its own site — the 17 named here are the ones its pages call out.
- Snowflake
- Databricks
- BigQuery
- Amazon Redshift
- PostgreSQL
- MySQL
- SQL Server
- Apache Airflow
- dbt
- Fivetran
- Looker
- Tableau
- Slack
- PagerDuty
- ServiceNow
- Microsoft Teams
- Webhooks
See all Monte Carlo integrations →
Alternatives to Monte Carlo
- Datadog Data ObservabilityA Datadog-native view of data quality, lineage, pipeline jobs, and costs.9.3
- IBM Data Observability by DatabandPipeline-focused observability with lineage, quality alerts, and Airflow integrations.9.1
- Actian Data ObservabilityWarehouse-native monitoring with custom SQL metrics, incident history, and lineage.9.0
See all Monte Carlo alternatives →
Monte Carlo vs the competition
- Monte Carlo vs Datadog Data Observability
- Monte Carlo vs IBM Data Observability by Databand
- Monte Carlo vs Actian Data Observability
- Monte Carlo vs Anomalo
- Monte Carlo vs Acceldata
- Monte Carlo vs Elementary
Compare Monte Carlo with any tool side by side →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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