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
Details
Sold by
Categories
Delivery method
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Buyer guide
Gain valuable insights from real users who purchased this product, powered by PeerSpot.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
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 |
Vendor refund policy
All fees are non-cancellable and non-refundable except as required by law.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Monte Carlo Data
By Collibra
By Atlan
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
Automated Data Discovery and Context Generation
Automatically ingests from AWS data estate including Redshift, S3, Glue, Athena, Lake Formation, and SageMaker to generate business context with certified definitions, lineage, ownership, and quality scores in two weeks.
Context Development Lifecycle Management
Provides Build, Test, Review, Approve, Deploy, and Learn stages where AI bootstraps context and simulates tests while domain experts resolve ambiguity and approve before deployment.
Multi-Agent Context Delivery Protocol
Delivers unified context through MCP Servers to multiple AI agents including Amazon Quick Suite, SageMaker Unified Studio, Claude, Copilot, Cursor, and Gemini via a single open protocol.
Native AWS Data Platform Integrations
Natively integrates with Amazon Redshift, S3, Glue, Athena, Lake Formation, and SageMaker Unified Studio, plus Snowflake, Databricks, dbt, Airflow, and leading BI platforms.
Compounding Learning Loop
Continuously improves context quality through memory, feedback, and traces from every agent interaction, enabling the context layer to become smarter with each query.
Standard contract
No
No
Customer reviews
Pradeep K
Data quality monitoring has saved verification time but still needs smarter rule guidance
Reviewed on Jun 22, 2026
Review provided by PeerSpot
What is our primary use case?
I work as a business analyst and I usually see data anomalies in our company's data set, and I also work a lot on Power BI reports to see our performance on the supplier side.
When we receive data from our suppliers to view their performance, sometimes the data is not complete or they are doing backfills of previous data, so we have established some rules in Monte Carlo to monitor these anomalies, and whenever we see something passing a preset limit, we receive an alert.
When I open Monte Carlo , I usually look at my dashboard to see how many alerts we have received in the last few days, but I usually check the alerts in the last month, and I see which rule has received the maximum amount of alerts; then I try to solve it first because the pattern is similar, and then I try to solve other alerts based on other rules.
In my team, it's me who handles those alerts, but we have another team who works on these alerts as well, although they are working on another kind of data set, but in the company, it's used by many people.
What is most valuable?
The best features of Monte Carlo for my work are the ability to see alerts clearly, how many alerts we have received on which rule and for which country, and there is a feature called investigation query inside Monte Carlo which shows a pre-done analysis, so you don't have to run an SQL query by yourself to do manual checks.
It gives a clear analysis in a large data set, which is very time-saving, so I don't have to run manual codes to verify the data, and it has helped me a lot in saving time and improving efficiency while doing data checks or verifying data. It's a really great tool to explain the anomalies when we see one in Monte Carlo, as we have actual proofs to show to people or to the managers that we are having this anomaly or that data is missing.
There's also an AI feature that is inbuilt in Monte Carlo, but you have to pay separately for that feature, and I used it for quite a while in the beginning, but now my company has disabled it.
What needs improvement?
The biggest pain point with Monte Carlo is that we have created some rules, but those rules cannot judge everything, and I think the platform is a bit complex for someone new, so it can be more intuitive; a display adoption platform could guide the user on how to use this, like a DAP system. It took a lot of time for me to learn it, and without a guide, a new user would be clueless.
If I could change one thing about Monte Carlo, it would be for the platform to suggest some data quality rules by itself or some algorithms based on the anomalies and the patterns of our anomalies, which would be helpful, and also changes in our rules according to past anomaly patterns. I think that would be good, and they should also improve their support system, as I find it a bit weaker at the moment.
For how long have I used the solution?
It's been six months since I've been working on Monte Carlo, and it's a really great tool for analyzing data quality anomalies.
Which solution did I use previously and why did I switch?
Previously, the data checks were performed manually; they extracted data on Snowflake and then did manual verifications on Excel using formulas.
How was the initial setup?
It was not me who implemented Monte Carlo; it was another senior data analyst who implemented it a year ago, but I think it took a few months to get everything up and running.
It needed formal training because the tool is not that easy; if someone doesn't have a data analyst or business analyst background, you have to explain every rule which you have set by yourself, because the rules are created by us, not Monte Carlo; Monte Carlo is just a tool. We put our own rules to govern the data sets, and we literally had to make a guide to help users get to know that platform.
What about the implementation team?
It was not me who implemented Monte Carlo; it was another senior data analyst who implemented it a year ago, but I think it took a few months to get everything up and running.
What was our ROI?
Monte Carlo saves me roughly 30% to 40% of my time in doing verifications or data quality checks, and related to my team, I'm the only one who uses it in my team, but we collaborate with another team that uses it.
Which other solutions did I evaluate?
I didn't ask that question in my company; it was a good choice, so it's a very popular tool, which is why my company picked Monte Carlo.
What other advice do I have?
I would like to leave a review for the tool called Monte Carlo, which is a data quality analysis tool.
I think they just changed the layout a little bit, and during my work, I didn't see any changes in the platform except for the AI feature, which I don't use anymore.
Our final goal is to automate every data quality manual check into Monte Carlo so we don't have to do a lot of checks by ourselves.
If I find something bizarre, I go to Monte Carlo and see if it's happening there as well, so it gives me confirmation that the issue is occurring and we can contact our supplier or the country to verify what it is.
Monte Carlo is a really good tool; whenever I do data verification checks for the dashboards, and if I find something bizarre, I go to Monte Carlo and see if it's happening there as well, so it gives me confirmation that the issue is occurring and we can contact our supplier or the country to verify what it is. I would rate this product a 7 overall.
Patel_Dhulva
Faced alert fatigue yet have improved data quality monitoring and faster root-cause analysis
Reviewed on Jun 19, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Monte Carlo is smart data observability and lineage that saves hours of debugging. What I like most about Monte Carlo is its automated data observability and lineage capabilities.
What is most valuable?
Monte Carlo's lineage feature has helped me save hours of debugging because its machine learning-driven alerts are incredibly effective. It quickly learns our database behavior and catches anomalies, freshness issues, or volume drops before our downstream users even notice.
Monte Carlo helps us catch data errors and broken dashboards before our team or clients notice them. I also love the intuitive user interface, which makes it easy to trace an issue from a Looker dashboard all the way back to our Snowflake warehouse. It has saved our data engineers team countless hours of manual debugging.
The best features that Monte Carlo offers include a highly intuitive user interface that makes it easy to trace an issue from a Looker dashboard all the way to our Snowflake warehouse. The platform's machine learning-driven alerting is incredibly smart because it quickly learns our data's baseline behavior and catches anomalies, freshness issues, or volume drops before our downstream users even notice it.
Monte Carlo integrates seamlessly with the major cloud data warehouses. It can configure deeper integration with some legacy on-premise systems or niche BI tools, which is valuable.
Monte Carlo has positively impacted our organization as it is a critical tool that integrates seamlessly with major cloud data warehouses. It is easy to trace an issue, and it has saved our data engineer team countless hours of manual debugging. It also helps us catch data errors and broken dashboards before our team or clients notice them.
What needs improvement?
While the machine learning-driven alerting is powerful, I find that the initial tuning phase in a complex Databricks environment can result in some alert fatigue.
The alert fatigue I experience is due to needing manual tweaking upfront to ensure our Slack channels are not flooded with false positives for expected volume fluctuations or batch variations.
I would appreciate a more streamlined interface, along with clear navigation and better sign-posting between sections, as this would improve the overall user experience.
For how long have I used the solution?
I have been working in my current field for five years.
What do I think about the stability of the solution?
Monte Carlo is stable, as I have not experienced much downtime or crashing.
What do I think about the scalability of the solution?
Monte Carlo handles our growing data needs very well, making it quite scalable.
How are customer service and support?
Monte Carlo's customer support is responsive and helpful. My experiences reaching out to them show that they were very quick to help and very professional.
Which solution did I use previously and why did I switch?
I previously used Datadog as a different solution.
I switched from Datadog to Monte Carlo because Datadog was somewhat complex, especially with the multiple features. Additionally, the customer support team was not as responsive compared to Monte Carlo's support, and the price was higher when compared to Monte Carlo.
How was the initial setup?
It was very easy to deploy Monte Carlo in our environment, with no challenges faced.
What was our ROI?
I have seen a return on investment because Monte Carlo has solved the challenge of monitoring ingestion health at scale. We are able to automatically track data freshness across hundreds of tables sourcing from multiple systems, which benefits us by eliminating manual data quality checks and providing real-time alerts the moment an ingestion pipeline lags, significantly reducing our data downtime.
What's my experience with pricing, setup cost, and licensing?
Monte Carlo provides a reasonable price plan, so I find it highly affordable for any organization sizes.
Which other solutions did I evaluate?
What other advice do I have?
My advice to others looking into using Monte Carlo is that it is a great tool for data quality and observability, ensuring our data is timely and complete. It is very user-friendly, combining low-code capabilities for business users with complex SQL for technical users. It is a highly recommendable tool. Monte Carlo is very easy to set up and very useful for detecting issues. Monte Carlo is a game changer. I would rate this product four out of five.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Manraj S.
Data Lineage and AI That Proactively Flags Freshness Issues and Abnormalities
Reviewed on Jun 11, 2026
Review provided by G2
What do you like best about the product?
The data lineage and AI features automatically detect data freshness issues and abnormalities.
What do you dislike about the product?
The 15min minimum latency for alerts for freshness and quality
What problems is the product solving and how is that benefiting you?
Data freshness and Data quality + Lineage is a plus
Vandan T.
Smart Data Observability and Lineage That Saves Hours of Debugging
Reviewed on Jun 09, 2026
Review provided by G2
What do you like best about the product?
What I like most about Monte Carlo is its automated data observability and lineage capabilities. The platform's machine learning-driven alerting is incredibly smart; it quickly learns our data's baseline behavior and catches anomalies, freshness issues, or volume drops before our downstream users even notice. The user interface is highly intuitive, making it easy to trace an issue from a Looker dashboard all the way back to our Snowflake warehouse. It has saved our data engineering team countless hours of manual debugging
What do you dislike about the product?
While Monte Carlo integrates seamlessly with major cloud data warehouses, configuring deeper integrations with some legacy on-premise systems or niche BI tools requires more manual configuration than expected. The documentation is generally good, but clearer step-by-step troubleshooting guides for edge-case integration errors would make the onboarding process even smoother
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us catch data errors and broken dashboards before our team or clients notice them. Before using it, we spent too much time manually checking our data and trying to find where mistakes happened. Now, it automatically alerts us the moment something looks wrong, which saves our team hours of troubleshooting every week and keeps our reports accurate
Prem T.
Proactive Data Observability That Catches Issues Early
Reviewed on Jun 09, 2026
Review provided by G2
What do you like best about the product?
Monte Carlo’s biggest strength is proactive data observability, it catches data issues early, before they hit dashboards or business decisions.
What do you dislike about the product?
It can feel overly expensive and exclusive, which makes it less welcoming for ordinary travelers.
What problems is the product solving and how is that benefiting you?
Monte Carlo solves core data reliability problems, like pipeline breakages, freshness delays, schema drift, and unexpected volume/distribution changes.