I like how Monte Carlo is self-sufficient and can learn from itself to improve by creating new rules and alerts. It's smart enough to look at data, identify alerts, and create new ones if necessary, which saves a lot of time and work because we don't have to dig into the data ourselves or make our own tools. Review collected by and hosted on G2.com.

I think the UI might be a little bit intimidating. There's a lot going on. It's has a lot of information packed, which isn't a bad thing, but to a beginner, it might look a little intimidating. Review collected by and hosted on G2.com.

I use Monte Carlo for data freshness and custom SQL monitoring. It helps me reliably track my assets' health status in case of any data quality issues or data ingestion delays. I like the troubleshooting agent using AI, which helps debug anomalies in data. I also appreciate the integration with other platforms like Airflow for failure updates. It's also easy to set up with credentials. Review collected by and hosted on G2.com.

The free operating agent is not up to the standards of the troubleshooting agent, which has a limit. The alert summary and advice are usually repetitive of the alert description and don’t carry any new information. Review collected by and hosted on G2.com.

I like that with Monte Carlo, we don't have to set thresholds manually for anomalies. Instead, we can rely on Monte Carlo to decide what is noteworthy for an alert. I also appreciate that it is pretty much plug and play, working out of the box with very little setup. The setup process is extremely easy, which is one of my favorite parts about the Monte Carlo platform. Review collected by and hosted on G2.com.

Noisiness is the biggest challenge. Tuning old alerts so we only get alerted about things that are truly noteworthy and need our attention. It's the biggest downside and thing we fight with. Review collected by and hosted on G2.com.

The built-in machine learning monitors that track freshness, volume, and schema changes are fantastic. I really appreciate how these features work right out of the box. Review collected by and hosted on G2.com.

To be completely honest, this is the best tool I have used for data observability and large-scale data quality checks. However, if I had to mention one drawback, it would be the extra features that come with the integrations. For example, MC attempts to display traces from our Airflow integration in several areas, but I have noticed that the information is not always accurate in some places. I have observed a similar issue with the dbt integration as well. Review collected by and hosted on G2.com.

I've found field lineage to be far more useful than I originally imagined. The table importance scale is also very nice to see. It has allowed us to get ahead of data quality alerts before our stakeholders are even aware of anything wrong. I find it easy to navigate especially and track down the most important models. There is a feature that let's you know if a query has changed based on the number of characters in a query, which is really nice. Review collected by and hosted on G2.com.

I really wish there was a way to snooze the monitors and alerts in the same manner, as it can sometimes become overwhelming. Review collected by and hosted on G2.com.

Monte Carlo has allowed us to monitor our data pipelines with increased clarity. One of its standout features is its ability to catch errors before they reach production, significantly reducing downtime and ensuring data integrity.

This product also played a crucial role in supporting our new client-facing data product. Its robust error detection and comprehensive reporting capabilities enabled us to launch with confidence, knowing that our data was accurate and reliable. Review collected by and hosted on G2.com.

The learning curve for setting up monitors, and understanding the system, was steeper than expected. Combined with the large number of tables in our warehouse, it was a laborious implementation process. Some of these issues are unavoidable. In the future I'm curious if there's a more efficient way to set up monitors. For example, in our case we set up the exact same rules for multiple tables, with the only difference being the field name and some slight variations in the SQL. Review collected by and hosted on G2.com.

I like how Monte Carlo brings data quality insights to the people who can fix them, the users of the data sources. I also find the ML thresholds helpful because they let Monte Carlo handle the error alerts, so the data platform team doesn't have to create the error thresholds manually. The integration with Slack is another plus, as it offers a centralized place for alerts and makes it easy to send them to the right stakeholders. Monte Carlo is easy to use, even though I didn't handle the initial setup. Review collected by and hosted on G2.com.

I'm having challenges with integrating Monte Carlo with AI agents. It would be great if AI agents could interact more seamlessly with Monte Carlo. Review collected by and hosted on G2.com.

I like Monte Carlo for its AI features that automatically handle the creation of boundaries when you select a source to be monitored. The automatic monitoring of schema changes, metric changes, and freshness is also great. I appreciate its integration with Slack, enabling the creation of automated workflows and keeping everyone informed proactively. The AI feature and automatic monitoring save a lot of time by eliminating the need to manually think about boundaries or constantly check for schema changes. Setting up the system was very easy, as all systems were connected quickly through admin accounts, taking less than a day. Review collected by and hosted on G2.com.

The main thing I don't like about Monte Carlo is how you need to select tables. We're really careful about what tables and sources we want to monitor, and that takes quite a lot of time. It's not super easy to navigate and select or deselect tables from a schema. That could be improved in my opinion. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Onboarding was made very east, ui and indepth source to target lineage, and ability to deep dive problems with ai Review collected by and hosted on G2.com.

Non ability to export the meta data gathered from analyzing our system, still early days but a better way to make the notifications feel meaningful versus something that's glazed over, seems to miss complete lineage in Tableau Review collected by and hosted on G2.com.