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.

-agentic monitoring capabilities is definitivelly interesting.

-Being able to create custom role is also very helpfull Review collected by and hosted on G2.com.

-still on visualizing the data and dashboarding capabilities are lacking compred to other competitors Review collected by and hosted on G2.com.

Easy to set up alerts. Integration with MS Teams Review collected by and hosted on G2.com.

I feel like the AI tuning of the alerts could be better 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.

Trend analysis and time series Quality checks Review collected by and hosted on G2.com.

UI is not that useful. But with coding agents I am able to create any QC monitors easily Review collected by and hosted on G2.com.

The alerts are pretty easy to confer and go through Review collected by and hosted on G2.com.

it can be a little overwhelming at the start 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.