Monte Carlo vs Bigeye: Data Observability Compared (2026)

Quick verdict

Short answer

Choose Monte Carlo if you want a broad, warehouse-first observability service with a published tier structure (Start, Scale, Enterprise, Business Critical) and a billing unit you can model before talking to sales: monitors, paid for with credits. Choose Bigeye if your estate mixes cloud warehouses with older platforms such as Teradata, Netezza, Db2 or Greenplum, you want column-level lineage across both, and you want monitor definitions in files with bigConfig. Neither has a free tier or self-service trial, so the practical test is a scoped proof of concept on your own critical tables.

How we know: This comparison is research-based: tiers, billing units, deployment options, connectors and monitoring features were checked on 7 November 2026 against Monte Carlo's pricing and integrations pages and documentation, and against Bigeye's home and pricing pages and its documentation at docs.bigeye.com. We have not run either product, so nothing here is a measured result.

Monte Carlo now operates at montecarlo.ai (montecarlodata.com redirects there) and sells itself as a data and AI observability platform. It reads metadata and query logs from warehouses, lakes, pipeline and BI tools, raises alerts on freshness, volume, schema and quality problems, and builds lineage from query history so an incident can be traced upstream to a job and downstream to a dashboard. It is SaaS, with a hybrid option in which an agent and a data store for sampled records run in your own AWS, Azure or GCP account.

Bigeye collects table and column metrics (bigConfig lists FRESHNESS and VOLUME among the table metrics) and sets thresholds automatically from history. Its Lineage Plus feature builds column-level lineage across modern and legacy sources, and lineage-enabled monitoring can extend coverage to the upstream dependencies of an important table. Many sources connect agentlessly; a self-hosted Data Source Agent on Docker or Kubernetes reaches warehouses you do not want exposed. Bigeye now describes itself as an "AI Trust" platform, adding sensitive data scanning and AI governance modules to monitoring.

Observability, not testing. Both tools watch production tables for unexpected change. They complement, rather than replace, explicit assertions in your pipeline code; see Datafold vs dbt tests and Soda vs Great Expectations.

Side by side

Aspect	Monte Carlo	Bigeye
Product model	SaaS data and AI observability platform	SaaS data observability platform, now marketed as an AI Trust platform
Deployment options	SaaS, or hybrid with a customer-hosted agent and data store on AWS, Azure or GCP; dedicated instance on Business Critical	SaaS with agentless connections or self-hosted Data Source Agents on Docker or Kubernetes
How monitoring works	Automated freshness, volume and schema monitors from metadata and query logs, plus user-defined monitors	Table and column metrics with thresholds learned from history, optional custom thresholds
Lineage	Built from query logs, linking tables to ETL jobs and BI reports	Lineage Plus: column-level lineage across modern and legacy sources
Monitoring as code	Not covered in the pages checked	bigConfig configuration files for monitors
Legacy platforms (documented)	Teradata, SAP HANA, Oracle (Oracle, SAP HANA and Teradata listed as Enterprise-tier integrations)	Teradata, Netezza, Db2, Greenplum, Yellowbrick, SAP HANA, Oracle
Dataset comparison	Not a stated feature	Deltas compares similar datasets for replication, migration or model changes
Alerting	Slack, Teams, PagerDuty, Opsgenie, Jira, ServiceNow, webhooks	Slack, Teams, PagerDuty, Jira, ServiceNow, webhooks
Pricing transparency	Four published tiers and limits; per-monitor credits; no amounts	No plans, units or amounts published
Free tier or trial	None advertised	None advertised; a two-minute product tour
Wider scope in 2026	Observability for AI agents and model outputs	Sensitive data classification, governance and AI policy enforcement

Key differences

Where each one looks for problems

Monte Carlo starts from metadata and query history. Its documentation describes, for example, collecting Snowflake metadata hourly from information schema views and query history, and Redshift query logs every 10 minutes. That gives broad coverage with little configuration: once a warehouse is connected, freshness, volume and schema monitors can run across many tables, and you add user-defined monitors for specific rules.

Bigeye starts from metrics on tables and columns. You choose (or let lineage-enabled monitoring choose) which assets to watch, and Bigeye learns thresholds from each metric's history, with custom thresholds when you need fixed limits. In practice both reach the same goal, alerts on unexpected change, but Monte Carlo emphasises wide automatic coverage, while Bigeye emphasises metric-level control and focused coverage around important assets.

Lineage depth and legacy estates

Both use lineage for root cause and impact. Monte Carlo builds it from query logs across warehouses, ETL tools and BI reports, and its integrations list is very long (warehouses, lakehouses, transactional databases, Kafka and Confluent, dbt, Airflow, Fivetran and BI tools); on its pricing page, Oracle, SAP HANA, Teradata and Microsoft Fabric appear among the EDW integrations of the Enterprise tier. Some integrations are marked coming soon or waitlist on the integrations page, so check the status of each one you need.

Bigeye stresses column-level lineage (Lineage Plus) and documents connectors for platforms that many observability tools skip: Teradata, Netezza, IBM Db2, Greenplum and Yellowbrick, alongside Snowflake, Databricks, BigQuery and Redshift. If your critical data still passes through an on-premises warehouse before reaching the cloud, that connector list is often the deciding factor.

Configuration as code and dataset comparison

Bigeye offers bigConfig, a configuration-as-code format, so monitor definitions can be reviewed in pull requests and versioned with the rest of your platform code. Its Deltas feature compares similar datasets, which helps during replications, migrations and model rewrites. Monte Carlo lists data exports and webhooks from the Scale tier and an API with daily call limits per tier (10,000 on Start up to 100,000 on Enterprise and Business Critical); the pages checked did not describe a monitors-as-code format, so ask for a demonstration if that matters to you.

Network access and where samples live

Monte Carlo describes a hybrid model in which an agent and a data store for sampled records run in your own cloud account, with Terraform modules provided, and lists self-hosted storage and PII filtering from the Scale tier. Business Critical adds a dedicated instance and disaster recovery with failover to another region. Bigeye handles the same concern with its Data Source Agent: a container you run on Docker or Kubernetes, managed from the Bigeye UI, so the warehouse does not have to accept connections from the vendor's cloud. Either way you run a component yourself; the difference is mainly in what is stored where, which you should confirm with each vendor's security documentation.

Pricing and licensing

Monte Carlo. montecarlo.ai/pricing lists four tiers. Start covers up to 10 users and up to 1,000 monitors with 10,000 API calls a day; Scale adds unlimited users, SSO, SCIM, audit logging, self-hosted storage, PII filtering, exports and webhooks; Enterprise adds multiple workspaces, chargebacks and EDW integrations such as Oracle, SAP HANA and Teradata; Business Critical adds a dedicated instance and cross-region disaster recovery. Every tier is billed per monitor through credits bought in advance, and the cost per credit depends on the tier. No currency amounts, per-credit rates, free tier or trial are shown; the page offers "Request pricing" and a demo.

Bigeye. bigeye.com/pricing shows no prices, plans or pricing units, and no free tier or trial: it offers a demo and a short interactive product tour. Pricing is by quote. Because Bigeye now sells governance and AI policy modules beside monitoring, ask for a quote that separates the observability modules from the rest, so you can compare like with like.

Pricing checked on the vendors' official pages on 7 November 2026. Prices change; confirm before buying.

Where each one leads

Monte Carlo strengths

Published tier structure with stated user, monitor and API limits
Very wide integration list across warehouses, databases, streaming, ETL and BI tools
Query-log lineage connects an alert to the jobs and dashboards it affects
Hybrid deployment keeps sampled records in your own cloud account; dedicated instance available
Broad automatic coverage from metadata with little per-table setup

Bigeye strengths

Documented connectors for legacy platforms such as Teradata, Netezza, Db2 and Greenplum
Column-level lineage (Lineage Plus) across modern and legacy sources
bigConfig keeps monitor definitions in version-controlled files
Deltas dataset comparison for migrations and replications
Self-hosted Data Source Agent for warehouses that must not be exposed

Limitations

Monte Carlo limitations

No currency prices, free tier or self-service trial
Per-monitor credit billing needs forecasting as coverage grows
SSO and SCIM start at Scale; Oracle, SAP HANA and Teradata integrations at Enterprise
Some integrations are marked coming soon or waitlist

Bigeye limitations

No published plans, pricing units, free tier or trial
Schema change monitoring was not documented in the pages checked
Self-hosted agents mean Docker or Kubernetes operations on your side
The wider AI governance scope makes quotes harder to compare with monitoring-only tools

When to choose each

Choose Monte Carlo if

Your estate is mostly cloud warehouses and lakehouses with many tables to cover quickly
You want lineage that spans ETL tools and BI dashboards
You want to know the tier structure and billing unit before a sales call
You need a dedicated instance or cross-region failover (Business Critical)

Choose Bigeye if

Critical data still lives in Teradata, Netezza, Db2, Greenplum or Oracle
Column-level lineage is a requirement
You want monitor definitions reviewed and versioned as code
You are validating a migration and want dataset comparison in the same tool

When neither is right

You want a free starting point inside dbt: see Elementary vs Metaplane.
Your main concern is wrong values inside tables that arrive on time: see Anomalo vs Monte Carlo.
You only need rule checks in pipelines: see Soda vs Great Expectations.
You are all-in on one platform: native options such as Databricks data quality monitoring or AWS Glue Data Quality may be enough.

Final recommendation

Bottom line

Both are enterprise purchases with no free entry point, so the choice turns on your estate. A mostly cloud estate with many tables and BI dashboards to cover points toward Monte Carlo, whose tier and credit structure is at least published. A mixed estate where legacy warehouses feed the cloud, and where column-level lineage and monitors as code matter, points toward Bigeye. Ask both for a proof of concept on the same ten critical tables and compare alert quality, setup effort and the full quote.

Frequently asked questions

Do Monte Carlo or Bigeye have a free plan?

No. On 7 November 2026 neither pricing page advertised a free tier or self-service trial. Monte Carlo offers a demo and pricing request; Bigeye offers a demo and a short product tour.

How is Monte Carlo billed?

Per monitor, through credits bought in advance and consumed at Monte Carlo's published consumption rates. The cost per credit depends on the tier (Start, Scale, Enterprise or Business Critical). No amounts are published.

Which one supports legacy warehouses better?

Bigeye documents connectors for Teradata, Netezza, IBM Db2, Greenplum and Yellowbrick as well as cloud warehouses. Monte Carlo covers Teradata, SAP HANA and Oracle, listed among Enterprise-tier integrations on its pricing page.

Can I keep monitors in Git?

Bigeye documents bigConfig for monitoring as configuration-as-code. The Monte Carlo pages checked did not describe an equivalent format, so ask in a demo.

Sources

Checked November 2026.

How we research comparisons: our editorial method.