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.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
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
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
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
Jaiwin S.
My team stopped firefighting and started building again
Reviewed on Feb 16, 2026
Review provided by G2
What do you like best about the product?
The automated lineage and alerting are really important for our team. It’s a huge relief to know we’ll be the first to find out about bad data or a schema change rather than getting a message from a stakeholder downstream.
What do you dislike about the product?
There is definitely a bit of a learning curve to understanding the more advanced custom SQL monitors. While the UI is generally intuitive, getting the most out of the deeper observability features takes a significant time investment
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us tackle data downtime by catching silent failures like schema changes or stale tables before they impact our executive dashboards. It has completely changed our workflow from being reactive and firefighting to proactively fixing issues before the business even notices something is wrong. This is gamechanger for us, helping us keep the business happy.
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.
Information Technology and Services
Automated Data Lineage That Visualizes Pipeline Impact Instantly
Reviewed on Feb 14, 2026
Review provided by G2
What do you like best about the product?
The best part is the automated data lineage and how it visualizes the impact of a broken pipeline before the stakeholders notice. Instead of manually tracing dependencies in complex SQL or Spark jobs that we currently have, I can instantly see which dashboards will be affected by a schema change or a stale table.
What do you dislike about the product?
The initial setup for custom monitors is tricky, especially when i tried to tune SQL rules for business logic. While the ML alerts are great, getting the manual thresholds just right without triggering took some trial and error.
What problems is the product solving and how is that benefiting you?
It is solving the problem of data downtime. Those periods where data is missing, inaccurate, or broken without anyone knowing until it's too late. It provides a safety net by monitoring for freshness, volume shifts, and schema changes across our entire stack, which has been a blessing for me
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