Sold by: Monte Carlo Data
Deployed on AWS
Data breaks. We ensure your team is the first to know and the first to solve with end-to-end data observability.
4.3
Overview
As businesses increasingly rely on data and AI to power digital products and drive better decision making, it's mission-critical that this data is accurate and reliable. Monte Carlo's Data + AI Observability Platform is an end-to-end solution for your data stack that monitors and alerts for data issues across your data warehouses, data lakes, ETL, business intelligence, and AI tools. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact, and notify those who need to know. By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster. Monte Carlo also provides automatic, field-level lineage and centralized data cataloging that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements.
Highlights
- Detect: Detect data quality issues before your stakeholders at each stage of the pipeline
- Resolve: Resolve data issues with out-of-the-box root cause and impact analysis, including end-to-end field-level lineage
- Prevent: Prevent data downtime proactively across your stack
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Deployed on AWS
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Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
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Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
Monte Carlo Credit | Monte Carlo's Data Observability Platform Credit | $50,000.00 |
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All fees are non-cancellable and non-refundable except as required by law.
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Updated weekly
By Monte Carlo Data
By Collibra
Top
10
In Data Governance
Top
10
In Data Catalogs, Data Governance
Top
10
In Data Catalogs, Data Governance
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
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Positive reviews
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Negative reviews
AI generated from product descriptions
Data Quality Monitoring
Machine learning-based monitoring and alerting for data quality issues across data warehouses, data lakes, ETL pipelines, business intelligence, and AI tools
Root Cause Analysis
Automatic root cause identification and impact assessment with end-to-end field-level lineage for data issues
Proactive Issue Detection
Proactive identification of data issues across the data stack before stakeholder notification
Data Lineage and Cataloging
Automatic field-level lineage tracking and centralized data cataloging for data asset accessibility, location, health, and ownership
Multi-Stack Integration
End-to-end observability platform supporting data warehouses, data lakes, ETL systems, business intelligence tools, and AI applications
AI Governance Framework
Active metadata-based governance with rules, processes and responsibilities to ensure ethical AI practices, mitigate risk, adhere to legal requirements, and protect privacy
Automated Data Lineage
End-to-end lineage tracking providing transparency into data transformation and flow across systems, including both summary-level business lineage and detailed technical lineage
Unified Data Catalog
Multi-cloud and hybrid environment data discovery with business context including data origin, ownership, usage patterns, and access to reports, AI models and data products
Data Quality Automation
Automated monitoring and rule management system for enterprise-wide data quality management replacing manual processes
Privacy and Compliance Workflow
Centralized automation of privacy workflows to operationalize privacy requirements and address global regulatory compliance
Data Asset Discovery and Search
Powerful search algorithms combined with browsing capabilities to make data assets including tables, views, BI dashboards, SQL snippets, pipelines, and business metrics instantly discoverable.
Automated Data Lineage Construction
Automatic parsing of SQL query history to construct data lineage and detect personally identifiable information (PII) data for dynamic access policy creation.
Data Quality Profiling
Automatic generation of data quality profiles with variable type detection, frequency distribution analysis, missing values identification, and outlier detection capabilities.
Multi-Platform Integration
Deep integrations with popular data tools including Snowflake, Redshift, Databricks, Looker, and Power BI to create a unified metadata workspace.
Data Governance and Access Management
Automated governance capabilities including PII detection and creation of dynamic access policies for managing data ecosystem permissions.
Standard contract
No
No
Customer reviews
Mahek .
Enhanced Data Reliability with Powerful Monitoring
Reviewed on Feb 19, 2026
Review provided by G2
What do you like best about the product?
I use Monte Carlo mainly for monitoring data quality and reliability across our data pipelines. I like that it helps us quickly detect anomalies, broken tables, or unexpected changes before they impact downstream analytics. I really appreciate the automated data monitoring and alerting—it surfaces issues without requiring constant manual checks. The visibility into data lineage and pipeline health makes debugging much faster. It integrates smoothly with existing data tools, making adoption easier for the team. The automated monitoring and alerting help me catch data anomalies quickly, fixing issues before they affect dashboards or business decisions. The data lineage feature is especially valuable because it shows how datasets are connected, making it easier to trace the root cause of a problem. Together, these features save a lot of troubleshooting time and improve overall confidence in our data.
What do you dislike about the product?
Sometimes the alerts can feel a bit noisy, especially when multiple related issues trigger at once, so better alert tuning or grouping would help. The initial setup and configuration also took some time to fully understand. Improving customization and making onboarding a bit more intuitive would make the experience even smoother.
What problems is the product solving and how is that benefiting you?
I use Monte Carlo to monitor data quality and reliability, catching anomalies early and reducing manual checks. It improves trust in our data, enhances visibility into data pipelines, and integrates with existing tools, which streamlines troubleshooting and response times.
Rc M.
Integrative Dashboards with Smooth Setup
Reviewed on Feb 14, 2026
Review provided by G2
What do you like best about the product?
I like Monte Carlo's integrations with SaaS products, especially with Databricks and Snowflake, which help us organize, predict, and respond effectively. The initial setup is good and straightforward.
What do you dislike about the product?
I find user management in Monte Carlo could be improved.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps with governance and organizes, predicts, and responds effectively by integrating with SaaS products like Databricks and Snowflake.
Computer Software
Clear, Actionable Alerts That Catch Data Issues Early
Reviewed on Feb 14, 2026
Review provided by G2
What do you like best about the product?
What I like best about Monte Carlo is how good it is about catching data issues before they become real problems. The alerts are clear and actionable, which saves a lot of time. It’s given us much more confidence in the reliability of our dashboards and reports.
What do you dislike about the product?
I’d like to see deep-level support for Spark on Databricks, when it comes to capturing column-level lineage for some of our more complex transformation jobs. While the high-level lineage is good, getting that granular detail sometimes requires more manual configuration than I’d prefer for a tool.
What problems is the product solving and how is that benefiting you?
It solves the problem of unreliable data and the fire drills that come with broken dashboards or failed pipelines. Instead of reacting to issues after stakeholders notice them, we can proactively detect and address anomalies early, helping us deliver business critical dashboards more smoothly.
Insurance
Makes Monitoring Our GCP Pipelines So Much Easier
Reviewed on Feb 09, 2026
Review provided by G2
What do you like best about the product?
The way Monte Carlo surfaces anomalies in data freshness and pipeline behaviour is extremely helpful. It lets our team catch quality issues before they impact downstream users. The custom SQL query alerts are very accurate, and they save me a lot of time by pointing me straight to where things are breaking.
What do you dislike about the product?
The email alert formatting is restrictive — it’s difficult to insert clean tables or richer layouts for downstream users. More Outlook‑style formatting support would be a big improvement
What problems is the product solving and how is that benefiting you?
For me, the biggest value is the strong integration with Google Cloud. Monte Carlo picks up on freshness and pipeline issues across our GCP stack without any extra overhead. The custom SQL alerts are also a huge benefit — they let me monitor exactly what matters for our engineering datasets and surface issues in a very targeted way. Together, these help me identify problems early and keep downstream users informed
Chris A.
Powerful Monitoring, Complex Setup
Reviewed on Feb 09, 2026
Review provided by G2
What do you like best about the product?
I really appreciate the monitoring feature in Monte Carlo. It's great because we can write custom alerts and emails that are integrated with Teams, making it really easy to keep our stakeholders informed about any data quality issues or key updates they're looking for. It's really powerful for understanding exceptions in the data, even those that aren't directly failures or major data quality issues, which our team finds very valuable.
What do you dislike about the product?
It would be great to integrate the alerts and monitoring section more closely. Some of the UI elements could do with improvements. The standard parts in the emails could be adjusted since they always indicate pipeline failure or warning, but sometimes they are just informational. I also wish it could be integrated closer to our data to avoid repeating the same code in various places.
What problems is the product solving and how is that benefiting you?
I use Monte Carlo to expose DBT warnings and monitor trends over time, create custom rules for data alerts, and inform stakeholders of data quality issues through Teams integration.