Monte Carlo Data + AI Observability Platform
Monte Carlo DataReviews from AWS customer
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Automatically Detects Data Anomalies with Ease
What do you like best about the product?
It automatically detects data anomalies.
What do you dislike about the product?
It could include more features, and at times it feels a bit complex for someone who’s new to it. Also, there seem to be fewer options for the actions you can take during an alert.
What problems is the product solving and how is that benefiting you?
It helps me see the Data Quality alerts, along with the related information.
Effortless Anomaly Detection, Minor Usability Tweaks Needed
What do you like best about the product?
I really appreciate how easy Monte Carlo is to use, which makes identifying what's wrong with the data straightforward. I like that it provides a quick way to configure default anomaly detection on data assets at scale. The initial setup was very easy, and we were able to start monitoring about 80% of our assets right away. It's also great that Monte Carlo integrates with tools like Looker and PagerDuty.
What do you dislike about the product?
The way monitors are defined and changed (the migration that happened recently) is a bit confusing. The distinction between built-in monitor and custom ones was a bit difficult to understand for some consumers. Also, the 'forced' training of data for anomaly detection is tricky, as a lot of users ask how to better train the data that Monte Carlo has to tweak the detection. There should be a way to configure the thresholds before the actual datasets get trained properly.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps me quickly identify anomalies in data, making it easy to configure default anomaly detection at scale. It's very easy to use and simplifies identifying data issues.
Flexible UI, Code, and API Editing That Fits Our Workflow
What do you like best about the product?
Can edit in UI and also through code and api
What do you dislike about the product?
alerts get too noisy, hard to tune and cannot adjust learning lookback window
What problems is the product solving and how is that benefiting you?
observability and trends, as well as data anomalies
Great Anomaly Alerts, but a Verbose UI and Weak Monte Carlo as Code Docs
What do you like best about the product?
Out of the box functionality. Good alerting on anomalies, has caught many incidents over time.
What do you dislike about the product?
The UI is not user friendly and pretty verbose. The documentation on Monte Carlo as Code is really poor.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps my team with alerts on data anomalies, primarily row volume anomalies. This has helped us identify multiple incidents.
Automates Validation with Minor Setup Hiccups
What do you like best about the product?
I appreciate the investigation query section in Monte Carlo, which is particularly helpful for running additional checks to identify the root cause of issues. I like the data product section where I can see all the monitors I've set up for a particular project. It automates a lot of our validation processes, making it easier to manage and analyze data.
What do you dislike about the product?
Every time I'm setting up a new monitor, I have to click the test button a couple of times because it says it failed. Eventually, it will pass, but it's quite laggy and a little annoying to deal with. Initially, it took some time for me to understand and get used to because I needed to understand capabilities, the different user roles, and how to find tables.
What problems is the product solving and how is that benefiting you?
Monte Carlo automates validation processes and helps identify root causes of issues with investigation queries.
Intuitive Data Observability
What do you like best about the product?
I like the ease of use of Monte Carlo, especially how setting up monitoring is very simple. The integration with external tools like Slack and Jira is top-notch, sometimes eliminating the need to go to the Monte Carlo website to interact with an alert for its entire lifecycle. The user interface is generally very user-friendly, with only a few minor exceptions. I also love the quick pace at which the Monte Carlo team responds to issues, bugs, feature requests, and improvement suggestions.
What do you dislike about the product?
The biggest pain point for me is the lack of possibility to merge alerts from metric monitors into one incident. We often have an issue that triggers many alerts, and we have to manage each alert separately, even though all have the same root cause. Since metric monitors are the backbone of Monte Carlo, it's really frustrating. This has been the case for a year and a half now. Another issue is the too fast and too big changes; I expected more stability at this stage. It's really difficult to keep up with paradigm shifts. For example, the change for Table monitors caused confusion. I recently ingested a big data set only to learn that tables are now monitored by default upon ingestion, which was contrary to previous behavior where you had to set up monitoring manually.
What problems is the product solving and how is that benefiting you?
Monte Carlo helps notice missing or improper data. It's easy to use, integrates with tools like Slack and Jira, and has a user-friendly UI. Before we haven't had real monitoring, so it's a game changer for us
Great Observability, Easy UI, and Solid Data Warehouse Integrations
What do you like best about the product?
Great Observabiliity Features, UI is very easy to be used and Monte Carlo provides multiple integration to all data warehouses
What do you dislike about the product?
Lack of new technology stack integrations
What problems is the product solving and how is that benefiting you?
Helping us with our daily data quality and data observability checks
Powerful Observability Tool with Room for Improvement
What do you like best about the product?
I like the automatic thresholds in Monte Carlo's monitors, which makes it easier not to worry about setting dynamic or fixed thresholds thanks to the automatic ML threshold feature. I also appreciate its integration with orchestration tools like Airflow and DBT, as this allows us to check on specific failures in our workflows. These features help solve our observability issues related to data quality.
What do you dislike about the product?
Monte Carlo is a great tool but it is very overwhelming. Recently, there have been a lot of changes that affect our processes, like API endpoints, UI, contract, and monitor settings. These changes make us work too much, and they don't share these changes ahead of time. I also don't like that Monte Carlo doesn't allow running SQL queries if the table is not enabled for monitoring. There are some tables we need in queries but don't need the default monitors. The initial setup was quite easy 4 years ago, but now it's not that easy. Alerts are very noisy, and it would be helpful to have a dashboard view to manage these alerts.
What problems is the product solving and how is that benefiting you?
Monte Carlo solves our observability in data quality, serving as a central place to implement priority monitors across environments.
Flexibility in Monitoring and Proactive Data Improvements
What do you like best about the product?
The out of the box monitors ensure that you are up and running with insights quickly, the custom monitors ensure that you can tailor individual needs to be as specific or wide as you need, and MC integrates with pretty much everything you need to integrate with
What do you dislike about the product?
Adoption by business units can be difficult and it's easy to alert on too many things
What problems is the product solving and how is that benefiting you?
Pinpointing data quality issues before the data and insights get to our stakeholders. it allows us to be more proactive in solving finding and solving data inconsistencies which helps our data and insights be trusted.
Enhanced Data Reliability with Powerful Monitoring
What do you like best about the product?
I use Monte Carlo mainly for monitoring data quality and reliability across our data pipelines. I like that it helps us quickly detect anomalies, broken tables, or unexpected changes before they impact downstream analytics. I really appreciate the automated data monitoring and alerting—it surfaces issues without requiring constant manual checks. The visibility into data lineage and pipeline health makes debugging much faster. It integrates smoothly with existing data tools, making adoption easier for the team. The automated monitoring and alerting help me catch data anomalies quickly, fixing issues before they affect dashboards or business decisions. The data lineage feature is especially valuable because it shows how datasets are connected, making it easier to trace the root cause of a problem. Together, these features save a lot of troubleshooting time and improve overall confidence in our data.
What do you dislike about the product?
Sometimes the alerts can feel a bit noisy, especially when multiple related issues trigger at once, so better alert tuning or grouping would help. The initial setup and configuration also took some time to fully understand. Improving customization and making onboarding a bit more intuitive would make the experience even smoother.
What problems is the product solving and how is that benefiting you?
I use Monte Carlo to monitor data quality and reliability, catching anomalies early and reducing manual checks. It improves trust in our data, enhances visibility into data pipelines, and integrates with existing tools, which streamlines troubleshooting and response times.
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