Sold by: Monte Carlo DataÂ
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.
Overview
As businesses increasingly rely on data to power digital products and drive better decision making, it's mission-critical that this data is accurate and reliable. Monte Carlo's Data 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, and business intelligence. 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
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
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)Â .
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
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 Monitoring
Machine learning-powered monitoring of data warehouses, data lakes, ETL, and business intelligence systems
Data Quality Detection
Proactive identification and assessment of data quality issues using advanced machine learning algorithms
Data Lineage
Automatic field-level lineage tracking with comprehensive visibility across data assets and transformation pipelines
Root Cause Analysis
Automated root cause identification and impact assessment for data quality and reliability issues
Data Governance
Centralized data cataloging with capabilities for tracking data asset accessibility, location, health, and ownership
AI Governance
Provides active metadata-driven governance framework for AI strategies with rules, processes, and responsibilities to mitigate risks and ensure ethical AI practices
Data Catalog Management
Enables comprehensive discovery and understanding of data assets across hybrid and multi-cloud environments with full business context and metadata insights
Automated Data Lineage
Offers end-to-end lineage tracking with complete transparency into data transformation and flow across systems, including summary-level and technical lineage details
Privacy Workflow Automation
Centralizes and automates privacy workflows to address global regulatory requirements and encourage collaborative data protection
Data Quality Management
Replaces manual processes with automated data monitoring and rule management to scale data quality across enterprise environments
Data Discovery
Powerful search algorithms for discovering data assets across tables, views, BI dashboards, SQL snippets, and pipelines
Data Lineage
Automated lineage construction through parsing SQL query history with intelligent bot-driven tracking
Data Governance
Automatic PII data detection with dynamic access policy creation and ecosystem management capabilities
Data Quality Profiling
Automated generation of data quality profiles with variable type detection, frequency distribution, missing value, and outlier analysis
Multi-Platform Integration
Deep integrations with data platforms including Snowflake, Redshift, Databricks, Looker, and Power BI for comprehensive metadata management
Standard contract
No
No
Customer reviews
0 ratings
0 AWS reviews
|
436 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Sangavi D.
Great Tool for Easy Data Tracking and Quality Monitoring
Reviewed on Oct 14, 2025
Review provided by G2
What do you like best about the product?
Tool for easy track table data with bench mark. Customised altering system helps on daily monitoring. Dashboard stand out on data quality check and table activities.
What do you dislike about the product?
With current usage i havent see dislike.
What problems is the product solving and how is that benefiting you?
Tacking the table wise data quality and deviation monitoring being challenging. With Monte Carlo it is ease to use all as single hand and dashboard and custom alter config makes the day easier.
Financial Services
Great User Interface and Customer Service
Reviewed on Sep 10, 2025
Review provided by G2
What do you like best about the product?
1. Great user interface, straightforward to understand the functions of different section.
2. The customer service is great. Jennifer and Demarcus are really helpful in answering our quesitons and providing suggestions on building what we need.
3. The Monte Carlo integrates well with Teams and Jira.
2. The customer service is great. Jennifer and Demarcus are really helpful in answering our quesitons and providing suggestions on building what we need.
3. The Monte Carlo integrates well with Teams and Jira.
What do you dislike about the product?
1. The methodology of machine learning tools in Monte Carlo could be more straightforward, and it would be great if we can choose the algorithm for machine learning.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps monitor the data quality of tables and reduce our efforts to check the data manually. It makes sure the data goes in and out of our model aligns with our expectations and prevents major data issues happening.
Airlines/Aviation
Quick Wins using Monte Carlo
Reviewed on Sep 02, 2025
Review provided by G2
What do you like best about the product?
Monte Carlo's out of the box monitors create a relatively easy way to set yourself up for some potential big wins. Alerting that your source volume shows a small dip below expectation can potentially uncover a big issue. Focusing on critical data first sets you up to avoid an overwhelming number of alerts as the product 'learns' your data.
What do you dislike about the product?
Monte Carlo is early in the process to support the integration of Data Observability and the supporting Data Pipelines' state.
What problems is the product solving and how is that benefiting you?
Highlights data issues which are not obvious. Provides the capability to allow us to manually create monitors for critical checks. Out of the box monitors means less work to set up the product.
Insurance
Great data observability with easy to use tool
Reviewed on Sep 01, 2025
Review provided by G2
What do you like best about the product?
I've found Monte Carlo straight forward to use and easy to implement. The team at Monte Carlo has been very helpful when we've been onboarding new data products and provide timely and effective feedback for new users.
Despite being a large financial institution, we've been able to integrate MC to our systems using the OOTB supported methods which has made it much more efficient than other third-party products.
The OOTB ML monitoring has been a real hit at my organization and has enabled us to identify a number of issues more quickly than we would have been able to previously.
I've also been impressed by the variety and number of new features that Monte Carlo are releasing which have been especially helpful with incident notification to Teams.
Despite being a large financial institution, we've been able to integrate MC to our systems using the OOTB supported methods which has made it much more efficient than other third-party products.
The OOTB ML monitoring has been a real hit at my organization and has enabled us to identify a number of issues more quickly than we would have been able to previously.
I've also been impressed by the variety and number of new features that Monte Carlo are releasing which have been especially helpful with incident notification to Teams.
What do you dislike about the product?
Some of the documentation around the API's could do with improving and the number of API's make it a little difficult to understand which API to use in which circumstance.
What problems is the product solving and how is that benefiting you?
We implemented MC to allow our data teams to more quickly understand if something was broken and a starting place for the investigation to fix it. This has meant that time to detection and time to recovery are now quicker than before. As part of our financial regulations, we need to provide a level of data quality across all of our core data products, and we were able to map MC's OOTB ML and other monitors to these requirements and provide our regulator with assurance that we are doing this.
Thomas H.
MC is excellent for autonomous asset monitoring
Reviewed on Aug 22, 2025
Review provided by G2
What do you like best about the product?
I like how scalable it is. We have onboarded our first wave of users and they have been able to easily create their own monitors. Our orgs overall trust in our data products has grown significantly.
What do you dislike about the product?
Honestly nothing at this time. I think we're still working throwing which assets we NEED to monitor vs which are just "nice to have".
What problems is the product solving and how is that benefiting you?
MC is our autonomous data quality checker. We have deprecated various scheduled Airflow jobs to monitor our datasets since MC just does it better. MC monitors also demonstrate the ability to grow over time and learn the asset better. We have high confidence in our products now and trust that there are no hidden anomalies.