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
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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.
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Software as a Service (SaaS)
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Customer reviews
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406 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.
Broadcast Media
Monte Carlo Handles Simple and Complex Data Observability Needs with Relative Ease
Reviewed on May 21, 2025
Review provided by G2
What do you like best about the product?
Monte Carlo handles the complex data monitoring tasks and allows us to utilize our own SQL and business rules. We monitor our data by multiple segments and Monte Carlo makes that easy, alerting us when things go sideways. The Monte Carlo team also listens to us when we have ideas for improving the product (and our monitoring), and is constantly enhancing their product to meet customer needs.
What do you dislike about the product?
It's hard to pick something I really dislike about Monte Carlo. We tend to use the anomalous detection more than hard/fast rules, and there are situations where we'd like a little more control over the acceptable ranges.
What problems is the product solving and how is that benefiting you?
Monte Carlo is allowing us to automate our data monitoring, which was previously done manually. This has allowed us to expand what we are able to monitor. It also has allowed us to look at additional aspects of the data that we couldn't do with a manual process.
Entertainment
Monte Carlo review 05-20-2025
Reviewed on May 20, 2025
Review provided by G2
What do you like best about the product?
Excellent connectors for analysis and monitoring.
Lineage is very good and readable.
Support is outstanding.
API documentation is generally good and the API explorer is nice.
Simple to set up monitors and add assets.
We are using Monte Carlo extensively already given its ease of use and ability to check for anomalies.
Lineage is very good and readable.
Support is outstanding.
API documentation is generally good and the API explorer is nice.
Simple to set up monitors and add assets.
We are using Monte Carlo extensively already given its ease of use and ability to check for anomalies.
What do you dislike about the product?
Adding descriptions to objects like monitors is basically missing. It would be helpful to have a title and description field vs. having a limited description field that acts as the title as well. It is messy and requires too much curation governance. Monitors and Assets should have this capability.
Also, the APIs are ok but doc should contain better examples for use.
Also, the APIs are ok but doc should contain better examples for use.
What problems is the product solving and how is that benefiting you?
The ability to track out of range thresholds allows us to catch issues with data faster. We can resolve problems before they impact clients.
Shirli M.
Catching Data Issues Before They Catch Us
Reviewed on May 18, 2025
Review provided by G2
What do you like best about the product?
Monte Carlo gives us proactive visibility into data issues before they impact downstream stakeholders. The automated monitoring across tables, columns, and freshness saves our team countless hours we used to spend manually checking data pipelines. The integration with tools like Slack and dbt makes it seamless to stay on top of data health without leaving our workflow
What do you dislike about the product?
While Monte Carlo is powerful, the UI can sometimes feel cluttered when navigating large numbers of monitors or incidents. Additionally, the alerting can occasionally be noisy until it’s fully tuned for our environment. More granular control over alert thresholds and grouping would make the experience even better
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us catch data issues—like broken dbt models, delayed ingestions, or unexpected schema changes—before they impact business decisions. This has significantly reduced fire drills, improved trust in our data, and freed up our BI team to focus on delivering insights instead of troubleshooting pipelines.
Michael B.
Smart Data Observability and Quality
Reviewed on May 16, 2025
Review provided by G2
What do you like best about the product?
Our Team loves the out of the box monitors in Monte Carlo, they make time to value much shorter and allow the product to start adding value quickly while you work with the Monte Carlo team on more targeted monitoring capabilities. Really can't stress enough how responsive and helpful the support team is.
What do you dislike about the product?
We do see some issues with our monitors in Monte Carlo from time to time where we are using them in non-standard use cases, generally these show up as data not matching our expectations within the monitoring results but every time this has come up so far we have been able to get to the bottom of it with help from the support team.
What problems is the product solving and how is that benefiting you?
Monte Carlo lets us know when our data is out of date or when there are unexpected updates/deletes in critical tables. It does these things out of the box letting us focus on more targeted quality checks.
Vignesh R.
Robust Monitoring Tool with Room for Alert Management
Reviewed on May 16, 2025
Review provided by G2
What do you like best about the product?
Monte Carlo provides a reliable, near real-time data observability layer that helps us catch pipeline issues before they affect stakeholders.
What do you dislike about the product?
In metric monitors, we are unable to edit the SQL queries once the monitors are enabled.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us proactively detect data quality issues such as missing data, schema changes, and failed jobs across critical pipelines.
Before Monte Carlo, identifying the root cause of broken reports or data discrepancies was reactive and time-consuming. Now, with automated monitoring and anomaly detection, we can quickly isolate and resolve issues, minimizing business impact and improving trust in our data.
It has significantly improved our team’s efficiency and data reliability across departments.
Before Monte Carlo, identifying the root cause of broken reports or data discrepancies was reactive and time-consuming. Now, with automated monitoring and anomaly detection, we can quickly isolate and resolve issues, minimizing business impact and improving trust in our data.
It has significantly improved our team’s efficiency and data reliability across departments.