At a glance
| Supported data platforms | Snowflake, Databricks, BigQuery, Redshift, Synapse, Teradata, ClickHouse, SAP HANA, PostgreSQL, MySQL, SQL Server, Oracle, S3 data lakes |
| Anomaly detection | Automated monitors that learn normal behaviour from metadata and query history |
| Freshness and volume monitoring | Yes, from metadata and query logs (hourly for most warehouses) |
| Schema checks | Yes, schema change monitoring |
| Validation rules | User-defined monitors; check rule types in a demo |
| Lineage and root cause | Lineage from query logs, linking tables to ETL jobs and BI reports |
| Alerting | Slack, Teams, PagerDuty, Opsgenie, Jira, ServiceNow, webhooks |
| Deployment | SaaS, or hybrid with customer-hosted agent and data store |
| Licensing | Annual subscription, per-monitor credits, four tiers, quote only |
Monte Carlo fits data teams with a large warehouse or lakehouse estate that want automated monitors, lineage and incident workflows across many tools, and that can buy an enterprise contract. There are no public prices or self-service trial, and billing is per monitor through prepaid credits, so model your monitor count before talking to sales.
Overview
Monte Carlo is a data observability service: it reads metadata and query logs from your warehouse, lake and pipeline tools and raises alerts when tables arrive late, change volume unexpectedly, change schema or show unusual values. Its documentation explains that, for example, Snowflake metadata is collected hourly from information schema views and query history, and Redshift query logs every 10 minutes. Lineage built from those logs links an incident to upstream jobs and downstream dashboards.
The platform is sold as SaaS. For teams that do not want the vendor reaching into their network directly, Monte Carlo describes a hybrid model in which an agent and a data store for sampled records run in the customer's own AWS, Azure or GCP account, with Terraform modules provided. In 2026 the vendor extended the product to observability for AI agents and their data, and an assistant it calls Bayes groups related alerts into a single issue with a suggested cause.
Key features
- Automated monitors for freshness, volume and schema changes, plus user-defined monitors for specific tables and rules
- Lineage built from query logs, linking warehouse tables to ETL jobs and BI reports for impact and root-cause analysis
- Broad connector list: warehouses, lakehouses, transactional databases, streaming (Kafka, Confluent), dbt, Airflow and BI tools
- Incident notifications to Slack, Microsoft Teams, PagerDuty, Opsgenie, Jira, ServiceNow and webhooks
- Data Profiler (generally available for Snowflake, Databricks, BigQuery and Redshift)
- Hybrid deployment with a customer-hosted agent and data store; dedicated instance on Business Critical
- Observability for AI agents and model outputs (agent frameworks and OpenTelemetry telemetry)
Integrations and ecosystem
Pros and cons
Pros
- Very wide documented integration list across warehouses, databases, ETL, streaming and BI tools
- Lineage connects an alert to the jobs and dashboards it affects
- Hybrid option keeps sampled records in your own cloud account
- Tiered plans make the enterprise controls (SSO, SCIM, audit logs) explicit
Cons
- No public prices, free tier or self-service trial
- Per-monitor credit billing needs forecasting as coverage grows
- SSO and SCIM start at the Scale tier, not on Start
- Several integrations are marked coming soon or waitlist on the integrations page
Pricing
Monte Carlo sells four tiers: Start, Scale, Enterprise and Business Critical. All tiers are billed per monitor through credits bought in advance and consumed at Monte Carlo's published consumption rates, and the cost per credit depends on the tier. No currency amounts are shown: the pricing page only offers "Request pricing" and a demo. Start covers up to 10 users and up to 1,000 monitors; Scale adds SSO, SCIM, audit logging and webhooks; Enterprise adds multiple workspaces and chargebacks; Business Critical adds a dedicated instance and cross-region failover. No free tier or self-service trial is advertised.
Pricing checked on the vendor's official page on 7 November 2026. Prices change; confirm before buying.
Who it is best for
Central data platform teams with many warehouse tables, pipelines and dashboards who want automated monitoring, lineage and incident workflows from one commercial service.
Typical use cases
- Detect late or partial loads in Snowflake or Databricks tables before morning dashboards refresh
- Trace a broken BI report back to the upstream dbt model or Fivetran sync that changed
- Alert on-call data engineers in PagerDuty or Slack when a critical table stops updating
- Monitor the data feeding AI agents and retrieval pipelines alongside warehouse tables
Who should look elsewhere
- Small teams that want to start free or run checks inside dbt: see Elementary or Soda.
- Teams that need a fully self-hosted product with no vendor-run control plane.
Verdict
Monte Carlo is one of the broadest commercial data observability services, with automated monitors, query-log lineage and a long integration list. The trade-offs are quote-only pricing billed per monitor and no free entry point, so it suits organisations ready for an enterprise contract rather than teams testing the idea.
Frequently asked questions
How is Monte Carlo priced?
By quote. Monte Carlo bills per monitor through credits you buy in advance, with a different cost per credit on the Start, Scale, Enterprise and Business Critical tiers. The pricing page shows no amounts and no free plan.
Does Monte Carlo need access to my data?
It mainly reads metadata and query logs. For sampled records, Monte Carlo describes a hybrid deployment where an agent and data store run in your own AWS, Azure or GCP account so samples stay there.
Which warehouses does Monte Carlo support?
Its documentation describes collection for Snowflake, BigQuery, Redshift, Databricks, Teradata, Azure Synapse, MotherDuck, Dremio and S3 data lakes, plus SQL Server, PostgreSQL, MySQL, Oracle, SAP HANA and Azure SQL Database.
Is montecarlodata.com the same company as montecarlo.ai?
Yes. montecarlodata.com pages redirect to montecarlo.ai, which is now the company's main site.
Sources
- Monte Carlo pricing
- Monte Carlo integrations
- Data collection details per integration (documentation)
- Monte Carlo hosting and deployment options (vendor blog)
- Data Profiler general availability (changelog)
Checked November 2026.
Listed in: Data Quality & Observability.
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