Monte Carlo Data + AI Observability Platform

What do you like best about the product?

I like the flexibility of being able to combine different inputs and use SQL. The AI also helps me because it solves things that I sometimes can't find at first glance, like correcting queries, fixing database crashes, or setting up alerts.

What do you dislike about the product?

At first, I struggled to understand the logic and terms because I am not a software engineering user. I also think that customer service and the AI supporting it could improve: when the AI doesn't know something, it escalates, but sometimes the response ends up being the same due to Monte Carlo's internal policies. The AI should take that more into account.

What problems is the product solving and how is that benefiting you?

I use Monte Carlo to monitor changes in my database and unusual actions related to fraud, and receive alerts almost in real-time. It's a first phase before taking alerts to production or when we don't have other alert methods.

What do you like best about the product?

The Slack integration is what I like most. I appreciate that I can get alerts directly in Slack and use them to troubleshoot issues quickly.

What do you dislike about the product?

The fact you cannot customise alerts thresholds with the provided monitors and need to create custom ones for that

What problems is the product solving and how is that benefiting you?

Getting alerts when tracked data shows anomalies helps us know right away when something unusual happens.

What do you like best about the product?

Alerts are pretty smart. I love that slack messages show up right along with each alert too

What do you dislike about the product?

I think sometimes the alerts could be understood by an AI as an ongoing issue so everyone knows any repeated alerts belong to the same category of issues

What problems is the product solving and how is that benefiting you?

Alerting on dbt pipelines. Gives us reliability in terms of data and has also helped me catch a couple of unexpected data issues

What do you like best about the product?

I like Monte Carlo for cost monitoring and appreciate how very easy the initial setup was.

What do you dislike about the product?

I don't like the cost visibility with Monte Carlo, it could definitely be improved. Also, I'm not a fan of the frequent product updates that happen without approvals, these can be quite frustrating.

What problems is the product solving and how is that benefiting you?

Monte Carlo helps with proactive data monitoring, source to target checks, and business ownership on data alerts.

What do you like best about the product?

Monte Carlo's ability to be proactive with its recommendations and find anomalies you don't even know to look for. Plus its AI capabilities make setup and troubleshooting a breeze, it does all the work for you.

What do you dislike about the product?

While Monte Carlo's AI and ML capabilities are strong, they need to spend a little more time on the UI to be a bit more user friendly. Their billing/credit logic is extremely complicated to understand and predict. The amount of historical data used in their algorithm is a lot lower than others in the market and are unable to perform seasonality of data because of it.

What problems is the product solving and how is that benefiting you?

The reason for bringing in Monte Carlo is twofold. One, the ability to be proactive in what we are monitoring and the ability to automatically triage and troubleshoot issues to help reduce the mean time to resolution.

What do you like best about the product?

Monte Carlo gives us confidence in the reliability of our data by making incidents easier to detect, investigate, and understand. I especially like how it centralizes freshness, quality, lineage, and alerting in one place, so teams can quickly see what broke, what was impacted, and where to focus. It helps reduce the time spent manually chasing data issues and makes data reliability feel much more operational and measurable.

What do you dislike about the product?

Monte Carlo is powerful, but it can sometimes feel noisy or hard to tune, especially when monitors generate alerts that are technically correct but not always actionable. The investigation workflows are useful, though they can require context from outside Monte Carlo to fully understand root cause. I’d also like clearer guidance on monitor configuration and prioritization so teams can focus more easily on the highest-impact data reliability issues.

What problems is the product solving and how is that benefiting you?

Monte Carlo helps solve the problem of finding and understanding data quality issues before they become bigger downstream problems. It gives visibility into freshness, volume, schema changes, lineage, and anomalies, which helps us catch broken pipelines or unexpected data changes faster. The main benefit is reduced time spent manually investigating issues. It helps teams understand impact, prioritize the right fixes, and build more trust in the data used for reporting, analytics, and decision-making.

What do you like best about the product?

The Ease of Use and Operations agents, along with the Monitor agents, make monitor setup much easier and more straightforward. The Troubleshoot agent is especially valuable for root cause analysis (RCA) when issues come up. I also like the integration with multiple connectors like Data360 and AI.

What do you dislike about the product?

For retrieving failure records for that particular monitor currently users should be dependent on UI. metadata such as pass/fail status, including these details in data exports would enable the creation of more custom dashboards. better integration with data360 new objects types + informatica/mulesoft connectors

What problems is the product solving and how is that benefiting you?

observability & data quality

What do you like best about the product?

Monte Carlo has transformed how we manage data reliability & Observability. Before adopting to it , we spent hours chasing broken pipelines and missing records. Now, issues are flagged in real time, with clear incident detection, triage workflows, and root cause analysis the helps us resolve them faster. The data lineage view makes it easy to see downstream impact, and the integrations with our existing stack were smooth. The dashboards give leadership confidence in the accuracy of our reporting, and the freshness and volume monitoring ensure we don't miss silent data issues. It's a platform that saves us time, reduces risk, and build trust in our analytics.

What do you dislike about the product?

Initial setup takes some effort, alerts can be noisy at first, some advanced features like lineage and triage require extra training to fully leverage.

What problems is the product solving and how is that benefiting you?

Monte Carlo solves centralized data quality by giving us proactive alerts and easy to use DQ dimensions., which saves effort and helps us act before issues impact business.

What do you like best about the product?

I really like how straightforward it is to use. I also like the table where it includes everything, from the old and new primary locations which has really been helpful plus the direct link to the tour, also that we get reports on time.

What do you dislike about the product?

For example, in the menu where you can see the progression of the work (like fixed and in progress etc), I feel like there are too many options, which makes it confusing. Also, regarding the graphs, I think it would be easier if we just kept the table.

What problems is the product solving and how is that benefiting you?

It’s been helping us as a locations team by keeping our eyes on edge cases and letting us identify and solve them.

What do you like best about the product?

Rich functionality and maturity in data observability space. Ease of use and easy to integrate with any data sources. MC helps to check our data assets can be trusted. By integrating various tools / pipelines, MC provides single window to monitor our data assets. Its rich UI and functionality ensures that tool can be easily used by developers or end-users. Highly recommended and much needed tool if trustworthy data is essential in an organization.

What do you dislike about the product?

Access model can be improved. For now, only developers access MC. Secondly data quality option can be improved with some additional options e.g. duplicate checks etc.,

What problems is the product solving and how is that benefiting you?

For data products, we have SLAs such as freshness, volume analysis etc., With MC, we dynamically check whether the table contains recent data.