Monte Carlo Reviews & Product Details

Monte Carlo makes it easy to catch and resolve data issues before they impact stakeholders. The automated data quality monitoring, lineage visibility, and alerting help us identify root causes quickly. The integration process was smooth, and the UI is intuitive enough that both technical and non-technical users can navigate it with ease. Their customer support team is responsive and genuinely helpful, which makes onboarding and ongoing use even better. Review collected by and hosted on G2.com.

Sometimes the initial alert volume can be high until fine-tuned, which may feel overwhelming for new users. While integrations are generally strong, a few niche connectors still require manual workarounds. Pricing can also feel steep for smaller teams, though the value is there once implemented. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

the filter available in performance and assets tab does not work as expected and often shows misleading numbers while filtering based on various tags. Review collected by and hosted on G2.com.

What I like best about Monte Carlo is its ability to proactively detect data issues before they impact our business, combined with an easy-to-use interface that makes monitoring data quality straightforward and efficient. The support team is also very responsive and helpful, which makes implementation and troubleshooting smooth. Review collected by and hosted on G2.com.

While Monte Carlo offers powerful features, sometimes the initial setup can be a bit complex and may require more detailed documentation or guided onboarding for new users. Also, adding more customizable alert options would enhance its flexibility. Review collected by and hosted on G2.com.

The flexibility and getting timely and reliable alerts for Volume, Schema and Freshness is useful. Able to tune the model is great. Review collected by and hosted on G2.com.

Not dislike, but couple of things that can be better:

1) Dashboards can be better in providing more actionable insights like most frequently failing tables or top 5 failing tables, under which schema, failing for what reason, frequently failing monitors, etc

2) It would be great if any updates made on alerts in Monte Carlo can flow into ServiceNow incidents

3) Additional integrations with files would be great, like if a file has not arrived, etc.

4) If we can have the model tuned for alerts much sooner than 2 weeks would be a welcome move. Review collected by and hosted on G2.com.

I like how scalable it is. We have onboarded our first wave of users and they have been able to easily create their own monitors. Our orgs overall trust in our data products has grown significantly. Review collected by and hosted on G2.com.

Honestly nothing at this time. I think we're still working throwing which assets we NEED to monitor vs which are just "nice to have". Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

I like that Monte Carlo is intuitive and easy to use, especially for people who come from a non-technical background. I also like that the Monte Carlo team is responsive and there is to help us. Review collected by and hosted on G2.com.

My biggest frustration with Monte Carlo is that there is no coding wrapper that can be used. It is only out of the box or SQL at this point, so more in depth checks are hard to implement. Review collected by and hosted on G2.com.

We've been using Monte Carlo for a couple of years now, and it's become an essential part of our data engineering toolkit. It delivered value almost immediately—helping us uncover data quality issues we didn't even know existed. Between the machine learning-driven anomaly detection, our custom domain-specific monitors, intuitive lineage and query history features, and excellent customer support, Monte Carlo plays a vital role in helping us meet our data quality goals. Review collected by and hosted on G2.com.

Monte Carlo moves quickly, and while we appreciate the pace of innovation, early on it sometimes felt like there was too much change all at once. Additionally, the platform has a wide range of features—which is a strength—but it can occasionally be challenging to remember where to find some of the more nuanced settings or controls. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.