Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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Monte Carlo has been a good tool for data quality and observability
What do you like best about the product?
It helps us catch data issues proactively
What do you dislike about the product?
No problems so far in the use of this software
What problems is the product solving and how is that benefiting you?
It helps us catch data quality issues proactively, which leads to trusts from our user base.
A valuable tool for catching data and performance changes proactively
What do you like best about the product?
I like the customization options of the platform best. We have been able to leverage it as a business performance monitoring tool, in addition to data completeness. The possible use cases are numerous.
What do you dislike about the product?
The largest downside of using Monte Carlo is the learning curve. It took a decent amount of prompting to get our business team comfortable with setting up alerts and using within our daily flow. The amount of options, while helpful, yielded a slower learning curve.
What problems is the product solving and how is that benefiting you?
Flagging to us when we have issues with our data builds and alerting when we have significant unexpected changes we need to take a deeper look into.
A tool with potential, but hindered by limitations currently
What do you like best about the product?
Low code/no code monitors on tables, which makes it easy to set up. Custom SQL monitors are also fairly straightforward to set up.
Allows for synergies in cases where multiple teams are using the same table for different models.
Customer support is quick to respond and acts on feedback promptly.
Allows for synergies in cases where multiple teams are using the same table for different models.
Customer support is quick to respond and acts on feedback promptly.
What do you dislike about the product?
Investigation tools for errors are very limited or maybe not intuitive
No python support so the type of checks that can be created becomes limited as well.
Lack of transparency for the machine learning thresholds and how each sensitivity level is calculated.
Dashboards not as useful/intuitive compared to something like Salesforce.
No python support so the type of checks that can be created becomes limited as well.
Lack of transparency for the machine learning thresholds and how each sensitivity level is calculated.
Dashboards not as useful/intuitive compared to something like Salesforce.
What problems is the product solving and how is that benefiting you?
The biggest problem Monte Carlo solves is transparency of data quality standards that can be viewed by anyone in the organization. For instance, we already had data validation methods that existed before Monte Carlo, but since they were owned by the teams that created the models, there was not a lot of transparency as to what data validation checks were implemented and whether they are sufficient. Monte Carlo really helps with this and makes sure the data quality is up to standard for all of our tables.
PowerFul Data Obervability Platform for Proactive Issue Detection
What do you like best about the product?
It's been a game changer for catching data issues early. We use it with Snowflake and the automated alerts for freshness, volume and schema changes save us a lot of firefighting. The data lineage view makes it easy to trace problems and the email alerts keep the team in the loop right away.
What do you dislike about the product?
Customer monitor setup can take a bit of time, and some advanced alerting needs better documentation.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps us quickly detect and resolve data quality and pipeline issues before they impact downstream reporting and analytics. It provides end to end data lineage, so we can track anomalies back to the root cause across our Snowflake environments. Automated freshness, volume, and schema change alerts keep the team proactive rather than reactive, reducing downtime and improving trust in our data products.
Central tool for jobs observability and data quality
What do you like best about the product?
Features including multiple tool integrations, aggregation of assets based on multipe grouping tactics, out of the box monitors for tables which provides freshness, volume and data availability
What do you dislike about the product?
the filter available in performance and assets tab does not work as expected and often shows misleading numbers while filtering based on various tags.
What problems is the product solving and how is that benefiting you?
It helps people from Data Operations in getting holistic view of all the assets whether it is the table or jobs/pipelines from dbt, databricks and astronomer and shows the lineages. This solves the problem of looking into the same domain in cluttered way. Also, data quality and freshness monitors allows us to get realtime alerts using internal machine learning algorithms which does not require manual monitoring which ensures correct data is being fed into the database objects.
Monte Carlo Handles Simple and Complex Data Observability Needs with Relative Ease
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.
Monte Carlo review 05-20-2025
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.
Catching Data Issues Before They Catch Us
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.
Smart Data Observability and Quality
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.
Robust Monitoring Tool with Room for Alert Management
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.
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