--- title: Monte Carlo Reviews meta_title: 'Monte Carlo Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 536 reviews by the users' company size, role or industry to find out how Monte Carlo works for a business like yours. aggregate_rating: rating_value: 4.3 review_count: 536 scale: '5' date_modified: '2026-08-13' parent_category: name: Monitoring url: https://www.g2.com/categories/monitoring --- # Monte Carlo Reviews **Vendor:** Monte Carlo **Category:** [AI Agent Observability Software](https://www.g2.com/categories/ai-agent-observability) **Average Rating:** 4.3/5.0 **Total Reviews:** 536 ## About Monte Carlo Monte Carlo is the agent trust platform, trusted by Nasdaq, Cisco, PepsiCo, and hundreds of enterprise organizations worldwide. Founded in 2019 and backed by leading investors, Monte Carlo pioneered data observability and has expanded into the full AI reliability stack. We're consistently ranked #1 in data observability on G2 — and we're built for what comes next. As enterprises scale from dozens to thousands of AI agents across mission-critical use cases, Monte Carlo monitors, troubleshoots, and improves both those agents and the underlying data powering them. Our platform covers the full trust stack — from the data pipelines feeding agents, to the context they retrieve, the decisions they make, and the outputs they produce — across four trust dimensions: context quality, performance, behavior, and outputs. Only Monte Carlo closes the full trust loop across both data and AI, and we meet enterprises wherever they are on the spectrum from human-guided oversight to fully autonomous operations. With 100+ integrations across Snowflake, Databricks, and the rest of your stack, you get full coverage without ripping anything out. Traditional monitoring tools stop at the pipeline or cover only one dimension of reliability — leaving teams to manually investigate, diagnose, and fix failures across disconnected tools. Monte Carlo closes that gap. Teams using Monte Carlo dramatically reduce time to detect and resolve data and AI incidents, scale monitoring coverage without scaling headcount, and build the internal trust that turns AI investments into real business outcomes. If your organization is serious enough about AI to put it in front of customers, executives, and critical decisions — Monte Carlo is the foundation it needs. ## Monte Carlo Pros & Cons **What users like:** - Users value the **ease of use** of Monte Carlo, praising its intuitive interface and helpful documentation. (104 reviews) - Users value the **custom alerts and integration** in Monte Carlo, enhancing stakeholder communication and data monitoring efficiency. (98 reviews) - Users find the **monitoring features** of Monte Carlo invaluable for catching data quality issues early and enhancing communication. (92 reviews) - Users appreciate the **custom alerting integration** in Monte Carlo, enhancing communication and data quality monitoring effectively. (72 reviews) - Users value the **easy setup and automated anomaly detection** of Monte Carlo, enhancing data quality and consistency monitoring. (49 reviews) - Data Lineage (46 reviews) - Users appreciate the **intuitive UI and extensive features** of Monte Carlo, making data monitoring effortless and effective. (46 reviews) - Integrations (45 reviews) - Easy Integrations (44 reviews) - Easy Setup (44 reviews) **What users dislike:** - Users find the lack of **manual threshold settings** for alerts limiting, complicating the adjustment of alert sensitivities. (58 reviews) - Users find the **alert overload** from Monte Carlo's automated monitors to be disruptive and requiring excessive tuning efforts. (57 reviews) - Users face challenges with the **inefficient alert system** , including issues with notifications and complex UI elements. (47 reviews) - Users find the **UX improvement** necessary, citing slow performance and disorganized features as major drawbacks. (46 reviews) - Users find that Monte Carlo has **limited functionality** for custom metrics and manual threshold settings, hindering deeper analysis. (36 reviews) - Users find the **limited features** of Monte Carlo restrictive, necessitating ongoing adjustments for better operational efficiency. (31 reviews) - Not User-Friendly (25 reviews) - Poor UI (25 reviews) - Poor User Experience (22 reviews) - Noisy Alerts (20 reviews) ## Monte Carlo Reviews ### 1. Monte Carlo Review **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** August 12, 2025 **What do you like best about Monte Carlo?** Monte Carlo has a great support team that has been willing to help us with questions and improvement requests. Their product has easy to use out-of-the-box tools like machine learning thresholds that we've found to be helpful as well. We are also experimenting with their agent observability tools which allow you to have better insight into what is really happening in agentic workflows. **What do you dislike about Monte Carlo?** My biggest frustration with Monte Carlo is that there isn’t a coding wrapper (Python) I can use. Right now it’s limited to out-of-the-box functionality or SQL, so it’s difficult to implement more in-depth checks. **What problems is Monte Carlo solving and how is that benefiting you?** One of our ongoing challenges has been making sure all the different teams have proper coverage for our IP. We have a lot of squads under Analytics, and this has helped us keep the process moving so we can consistently ensure our products are covered appropriately. Also, in terms of Agent Observability, LLM interactions can be a bit of a black box for validation teams. We implemented an internal judge system for LLM-based projects, but Monte Carlo has also helped us get the big picture on how well our models are performing. ### 2. Great product for any organization that values data standards and quality **Rating:** 4.5/5.0 stars **Reviewed by:** Larry F. | Analytics Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 28, 2025 **What do you like best about Monte Carlo?** 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. **What do you dislike about Monte Carlo?** I really wish there was a way to snooze the monitors and alerts in the same manner, as it can sometimes become overwhelming. **What problems is Monte Carlo solving and how is that benefiting you?** It has been instrumental in being ahead of our stakeholders when it comes to changing data or data inconsistencies. Being on the data platform team, it's our responsibility to ensure robust and useable data for everyone, they trust the data that we provide and we must maintain high standards for our team and company so that stakeholders can make high impact choices. ### 3. Intelligent Monitoring, Needs Easier Navigation **Rating:** 4.0/5.0 stars **Reviewed by:** Lisa S. | Manager Data Analytics, Mid-Market (51-1000 emp.) **Reviewed Date:** April 19, 2024 **What do you like best about Monte Carlo?** 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. **What do you dislike about Monte Carlo?** 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. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo saves us time by eliminating the need to manually write tests. It uses AI for automatic boundary adjustments and integrates with Slack for proactive communication. Its automatic monitoring alerts us to schema changes, which helps prevent issues. ### 4. Enhanced Data Reliability with Powerful Monitoring **Rating:** 4.0/5.0 stars **Reviewed by:** Mahek . | Small-Business (50 or fewer emp.) **Reviewed Date:** February 19, 2026 **What do you like best about Monte Carlo?** 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 Monte Carlo?** 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 Monte Carlo 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. ### 5. Makes Monitoring Our GCP Pipelines So Much Easier **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Insurance | Enterprise (> 1000 emp.) **Reviewed Date:** February 08, 2026 **What do you like best about Monte Carlo?** The way Monte Carlo surfaces anomalies in data freshness and pipeline behaviour is extremely helpful. It lets our team catch quality issues before they impact downstream users. The custom SQL query alerts are very accurate, and they save me a lot of time by pointing me straight to where things are breaking. **What do you dislike about Monte Carlo?** The email alert formatting is restrictive — it’s difficult to insert clean tables or richer layouts for downstream users. More Outlook‑style formatting support would be a big improvement **What problems is Monte Carlo solving and how is that benefiting you?** For me, the biggest value is the strong integration with Google Cloud. Monte Carlo picks up on freshness and pipeline issues across our GCP stack without any extra overhead. The custom SQL alerts are also a huge benefit — they let me monitor exactly what matters for our engineering datasets and surface issues in a very targeted way. Together, these help me identify problems early and keep downstream users informed ### 6. Efficient Anomaly Detection with Monte Carlo **Rating:** 4.5/5.0 stars **Reviewed by:** Amit S. | Data Engineer **Reviewed Date:** February 04, 2026 **What do you like best about Monte Carlo?** I use Monte Carlo for setting up alerts if there's any data anomaly in our existing database tables compared to previous trends. I liked the alert system because it supports both time-based and event-based triggers. The monitor section and investigation section are very helpful. A huge benefit is the ability to create alerts based on our custom SQL. **What do you dislike about Monte Carlo?** Monte Carlo sets up the alert based on the threshold decided by the past trend of the data, but we can't set any manual threshold for the alert. It should have both the functionality like the alert itself decides the threshold based on previous data trend which it already have and very useful. Another is setting manual threshold for some of the alert which is not present. **What problems is Monte Carlo solving and how is that benefiting you?** I use Monte Carlo to set up alerts for data anomalies, reducing daily manual intervention because we only check data if there's an alert. ### 7. Clear, Actionable Alerts That Catch Data Issues Early **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** February 14, 2026 **What do you like best about Monte Carlo?** What I like best about Monte Carlo is how good it is about catching data issues before they become real problems. The alerts are clear and actionable, which saves a lot of time. It’s given us much more confidence in the reliability of our dashboards and reports. **What do you dislike about Monte Carlo?** I’d like to see deep-level support for Spark on Databricks, when it comes to capturing column-level lineage for some of our more complex transformation jobs. While the high-level lineage is good, getting that granular detail sometimes requires more manual configuration than I’d prefer for a tool. **What problems is Monte Carlo solving and how is that benefiting you?** It solves the problem of unreliable data and the fire drills that come with broken dashboards or failed pipelines. Instead of reacting to issues after stakeholders notice them, we can proactively detect and address anomalies early, helping us deliver business critical dashboards more smoothly. ### 8. Robust Data Quality with Some SQL Limitations **Rating:** 4.5/5.0 stars **Reviewed by:** RAHUL B. | Senior Engineer (data platform) **Reviewed Date:** February 04, 2026 **What do you like best about Monte Carlo?** I like the ML-based anomaly detection and the ease of setting up data quality monitors in Monte Carlo. The web hook integration and data lineage features are valuable, especially for helping my data operations team troubleshoot issues by digging through data discrepancies. The process of setting it up was fairly straightforward. **What do you dislike about Monte Carlo?** Column lineage is a bit limited with complex SQL and can be improved. An example is if there is a switch case where source data could be sourced based on condition, it is not yet supported. **What problems is Monte Carlo solving and how is that benefiting you?** I use Monte Carlo for data observability and governance. It solves data quality, validation, and anomaly detection issues. The ML-based anomaly detection helps find unexpected data volumes, and data lineage aids in troubleshooting discrepancies by tracing data through its lifecycle. ### 9. Huge time saver for our team **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Consumer Goods | Enterprise (> 1000 emp.) **Reviewed Date:** December 17, 2025 **What do you like best about Monte Carlo?** I like that we don't have to write our own DQ rules from scratch and its organized in a user-friendly UI. The data quality dashboard is a very useful tool to show executives and prove the ROI for the software. **What do you dislike about Monte Carlo?** It can be complicated and overwhelming to understand the process as a whole on what to monitor, when to alert and what priority to assign. The popularity score doesn't always match with what the business considers our most important data and using the key asset tag doesn't allow the granularity to adjust how important an asset is. The AI features could use some work as they often offer suggestions that are not entirely helpful. **What problems is Monte Carlo solving and how is that benefiting you?** The ability to test data quality in several dimensions on our bronze and gold layers without having to manually do this in Snowflake is a huge time savings for our team. The proactive monitoring has helped us catch data development errors before it reaches our end user. To have this summarized in a dashboard with an overall data quality score is a very helpful benchmark. ### 10. Great Tool For Automated Detection and Custom Monitors **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Enterprise (> 1000 emp.) **Reviewed Date:** January 31, 2025 **What do you like best about Monte Carlo?** The depth of the monitors is excellent. The out-of-the-box ML stuff is great and spots changes that would normally go completely under the radar. On top of that, we can set up our own custom monitors for very specific business rules we need to check. It's a great mix of automated detection and hands-on control. **What do you dislike about Monte Carlo?** Since we want coverage across all our assets, the alerts we get can get pretty noisy. It feels like we're trading full coverage for a very busy channel. I think this could be improved by making the monitor configuration a bit more intuitive. It can be hard to figure out how to best set the tolerances to avoid false positives, and some in-line examples or better guides would be a huge help in reducing the noise. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo helps us catch data quality issues in our warehouse before they blow up and impact our users. Previously, we'd often find out about a problem only after a user complained or a key report was broken. Now, we're almost always the first to know and can jump on a fix immediately. The biggest benefit has been a real boost in how much our users—and our own team—trust the data. ### 11. Proactive Data Quality Monitoring That Saves Time **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consumer Goods | Enterprise (> 1000 emp.) **Reviewed Date:** December 12, 2025 **What do you like best about Monte Carlo?** Monte Carlo gives us end-to-end visibility into data quality across pipelines without needing to manually build monitoring for every table. I like how quickly it surfaces anomalies, schema changes, and freshness issues, and the fact that it integrates well with Snowflake. It saves a ton of time by proactively notifying us before downstream teams are impacted. **What do you dislike about Monte Carlo?** Some configuration areas still feel a bit “black-box,” meaning it can be hard to understand exactly why certain monitors trigger or why certain tables aren’t automatically covered. The UI can also feel somewhat cluttered at times, and alerting can get noisy until you fully tune everything. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo helps us quickly detect data breaks caused by upstream changes, ingestion failures, schema drift, and unexpected drops or spikes in record counts. It also centralizes data quality visibility across our Snowflake environment so the team no longer spends hours manually reconciling data issues or waiting for downstream teams to report problems. This leads to faster root-cause analysis, fewer broken dashboards, and maintains trust in our data products. ### 12. Data quality checks **Rating:** 5.0/5.0 stars **Reviewed by:** Lukasz W. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** January 30, 2025 **What do you like best about Monte Carlo?** The montecarlo is giving me a lot of posibilities in terms of data quality. I can setup the notifications, create a groups of people and send them a notification if something failes **What do you dislike about Monte Carlo?** As the user of google chat I'm realy anoyed that I need to use emails. For me the best way for alerts will be a direct message to a google chat group. The best way will be use the webhooks that google is providing **What problems is Monte Carlo solving and how is that benefiting you?** The montecarlo can compare few datasets and can send me a notification if I have less data or more. It is helping me to not make a huge mistakes for example it will send me the aler if the data from the table has been droped ### 13. MC Review **Rating:** 5.0/5.0 stars **Reviewed by:** Jay P. | Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** July 24, 2024 **What do you like best about Monte Carlo?** It can alert quickly, accurately, and easily. Monte Carlo can send an alert to Slack, which everyone checks daily, allowing someone to react more rapidly. **What do you dislike about Monte Carlo?** I hope there is a function that Monte Carlo can write a table into Snowflake. For now, I need to set up a dbt to create a table and an alert from Monte Carlo, which is not really convenient. **What problems is Monte Carlo solving and how is that benefiting you?** It is solving the data quality issue coming from vendors, alerting any data pipeline issues(where etl fail to run or abnormality) and also it alert the abnormal business activity as well. ### 14. Effortless Alerting, Reliable Performance, Needs More Alert Customization **Rating:** 4.0/5.0 stars **Reviewed by:** Jonny D. **Reviewed Date:** December 12, 2025 **What do you like best about Monte Carlo?** I use Monte Carlo for data quality and consistency monitoring. I like that it's very easy to set up alerts and get notified of problems. The product itself has been very stable and consistent, and runs with no issues. We integrate it with our data warehouse (Redshift), Slack, and email. The initial setup was very easy, and even though the cost is somewhat high, I really like the product. **What do you dislike about Monte Carlo?** I wish there was more nuance around the ability to set conditional alerts, such as 'if this fails 2+ days in a row with the same issue, stop alerting'. The cost is somewhat high. **What problems is Monte Carlo solving and how is that benefiting you?** I use Monte Carlo for data quality and consistency monitoring; it alerts us via Slack when custom jobs fail, so we don't have to check logs manually. ### 15. Great Data Observability Tool **Rating:** 5.0/5.0 stars **Reviewed by:** Vaibhav C. | Lead Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 15, 2025 **What do you like best about Monte Carlo?** The ability to view the model lineage, tests, and alerts within a single application is the most valuable asset for any data team. We have been using MC extensively and would not be able to function without it. **What do you dislike about Monte Carlo?** Too many alerts..Wish it was smarter in aggregating the alerts **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo is helping us serve better data to our product teams and the business by enabling us to see real-time data quality issues. ### 16. A tool with potential, but hindered by limitations currently **Rating:** 2.5/5.0 stars **Reviewed by:** Muhammad Yousaf T. | Quantitative Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** August 08, 2025 **What do you like best about Monte Carlo?** 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. **What do you dislike about Monte Carlo?** 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. **What problems is Monte Carlo solving and how is that benefiting you?** The biggest problem Monte Carlo solves is transparency of data quality standards that can be viewed by anyone in the organization. For instance, we already had data validation methods that existed before Monte Carlo, but since they were owned by the teams that created the models, there was not a lot of transparency as to what data validation checks were implemented and whether they are sufficient. Monte Carlo really helps with this and makes sure the data quality is up to standard for all of our tables. ### 17. Fast Monitor Creation and Smart Anomaly Detection **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Sports | Mid-Market (51-1000 emp.) **Reviewed Date:** December 16, 2025 **What do you like best about Monte Carlo?** I really enjoy how it allows me to create monitors very fast and the platform has agents to find anomalous towards my tables and data in general. The lineage mapping is also very nice **What do you dislike about Monte Carlo?** I wish the jobs was easier to put in, as if there was a way to dump all my jobs from a certain platform like snowflake or agilitke **What problems is Monte Carlo solving and how is that benefiting you?** I have lots of ingestion pipelines that I previously would not know if they went down. Now I know within a day when they went down. Data completeness is also great so I know that there aren't nulls in my database and that my metadata is up to date ### 18. Proactively Catches Silent Data Issues and Saves Hours of Troubleshooting **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** February 04, 2026 **What do you like best about Monte Carlo?** The best part is how it proactively catches silent data issues, like schema changes or unexpected volume drops, before my stakeholders even notice. It saves our team hours of manual troubleshooting time. **What do you dislike about Monte Carlo?** The initial setup and fine-tuning of monitors can feel a bit noisy, sometimes leading to alert fatigue if you don't stay on top of the configuration. **What problems is Monte Carlo solving and how is that benefiting you?** It solve the data downtime problem by catching pipeline breaks and schema changes before they reach our executive dashboards. This has significantly increased our team's productivity. ### 19. Easy to Use with Helpful Support, But Needs More Advanced Features **Rating:** 3.0/5.0 stars **Reviewed by:** Alice H. | Senior Data Engineer I, Mid-Market (51-1000 emp.) **Reviewed Date:** December 17, 2025 **What do you like best about Monte Carlo?** It's easy to use & setup. Customer support button is easy to find. There seem to be a decent number of features. **What do you dislike about Monte Carlo?** There could be more advanced features and the ease of integration could be better. **What problems is Monte Carlo solving and how is that benefiting you?** We are currently facing data quality challenges, including difficulties in identifying duplicates and ensuring observability. This is largely because, as a team, we have not yet established clear standards. ### 20. Intuitive UI That Catches Issues Before They Hit the Pipeline **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Insurance | Enterprise (> 1000 emp.) **Reviewed Date:** February 04, 2026 **What do you like best about Monte Carlo?** I really enjoy the intuitive UI. I also like that it helps catch issues early, before they make their way into the pipeline, which makes the overall process feel smoother. **What do you dislike about Monte Carlo?** I do wish Monte Carlo were more “set and forget.” In the early phase, acknowledging incidents can take a while, especially with the number of monitors we’ve set up. I also wish there were a cooldown period after setting up a monitor in Monte Carlo, so the training data could keep learning until it’s truly “ready.” **What problems is Monte Carlo solving and how is that benefiting you?** Identifying issues before it occurs. Seeing where the issue falls and speeding up my investigations help save my time. ### 21. I’m a BI Developer in MoonActive, using Mone Carlo to observe my company’s data **Rating:** 5.0/5.0 stars **Reviewed by:** Itay C. | BI Developer, Enterprise (> 1000 emp.) **Reviewed Date:** April 25, 2025 **What do you like best about Monte Carlo?** I’ll say few positive things: 1. The UI is very good, easily can create custom alerts and to investigate the data 2. The ability to connect MC with many alerts pipes such as Slack, mail 3. The sensitivity feature that allow us as data users to control when MC alerts will take action **What do you dislike about Monte Carlo?** 1. Sometimes the default alerts sensitivity is too high, and then I get spam alerts (for example added 10K rows, usually 10.5K rows). I’ll prefer that the default will be less sensitive 2. The custom alerts title to Slack requires 1 row, which requires aggregation of the query into a single row. It would be more convenient if MC would already take the column and collapse it into a single row automatically w\o using SQL function **What problems is Monte Carlo solving and how is that benefiting you?** 1. Automatic monitoring of my DB even when new tables are added without the need for manual intervention 2. The ability to automatically identify anomalies in the data 3. The alerts are dynamic - important tables will be marked with a star, data that has been sorted out will be marked with ״normalized״. This really helps me pay attention and emphasize important things ### 22. Great User Interface and Customer Service **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** September 10, 2025 **What do you like best about Monte Carlo?** 1. Great user interface, straightforward to understand the functions of different section. 2. The customer service is great. Jennifer and Demarcus are really helpful in answering our quesitons and providing suggestions on building what we need. 3. The Monte Carlo integrates well with Teams and Jira. **What do you dislike about Monte Carlo?** 1. The methodology of machine learning tools in Monte Carlo could be more straightforward, and it would be great if we can choose the algorithm for machine learning. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo helps monitor the data quality of tables and reduce our efforts to check the data manually. It makes sure the data goes in and out of our model aligns with our expectations and prevents major data issues happening. ### 23. Real-Time Anomaly Detection That Delivers **Rating:** 4.5/5.0 stars **Reviewed by:** Jomar A. | Manager - Data Operations, Enterprise (> 1000 emp.) **Reviewed Date:** December 11, 2025 **What do you like best about Monte Carlo?** Detecting anomalies, and sending real-time alerts **What do you dislike about Monte Carlo?** During our training sessions with the MC Team, there are several items that aren’t feasible but have workarounds **What problems is Monte Carlo solving and how is that benefiting you?** Data Analysis ### 24. Scalable monitoring and proactive support **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Online Media | Mid-Market (51-1000 emp.) **Reviewed Date:** January 23, 2024 **What do you like best about Monte Carlo?** Monitoring hundreds of tables is made easier by the simple setup and machine learned rules that work out of the box. Coupled with the ability to drill down and create custom rules specific to our business allows the Data Engineering team to be aware of critical issues and reduce time to resolution. Grouping tables and alerts is made simpler to find related issues, including monitoring of airflow pipelines, and BI assets. Giving the ability to determine business impact, routing the the notifications to appropriate stakeholders. Performance monitoring and integrations give us a holistic view of the data stack in our organisation. Customer support have been responsive, and customer success works regularly with us to learn our challenges and make suggestions to better use the platform. **What do you dislike about Monte Carlo?** - The amount of data to work through can be challenging at first, - The catalog feature is a bit limited. - Filtering which tables to enable monitoring, which impact cost, can be challenging. **What problems is Monte Carlo solving and how is that benefiting you?** - Monitoring for stale data - Identifying data anomalies - Discovering key issues ### 25. Proactive Data Reliability That Keeps Us Ahead **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Human Resources | Mid-Market (51-1000 emp.) **Reviewed Date:** December 18, 2025 **What do you like best about Monte Carlo?** Monte Carlo has helped our team maintain a much better sense of data reliability, as issues and changes in the data are now alerted to us proactively, we now have the chance to get things fixed before stakeholders even notice, rather than being reactive to their tickets about something being broken. **What do you dislike about Monte Carlo?** Out of the box, we were a little overloaded with alerts that didn't actually signify anything of importance leading to alert fatigue, luckily the customization options gave us the opportunity to remedy that **What problems is Monte Carlo solving and how is that benefiting you?** Data Observability, Proactiveness ### 26. Good Overall, But Custom Metric Limitations Hold It Back **Rating:** 3.5/5.0 stars **Reviewed by:** Xavier O. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** December 19, 2025 **What do you like best about Monte Carlo?** It's really easy to set up different kind of monitoring alerts. **What do you dislike about Monte Carlo?** Custom metric is a bit limited if you wat to do comparisons between fields within the same table. **What problems is Monte Carlo solving and how is that benefiting you?** It's really easy to set up different kind of monitoring alerts. ### 27. Montecarlo has helped us have more detailed insights on our data. **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Internet | Mid-Market (51-1000 emp.) **Reviewed Date:** August 19, 2025 **What do you like best about Monte Carlo?** Montecarlos anomoly detection has helped us correct small data discrepencies that we didnt know existed in our pipelines. The integration with our slack has helped us be notified of discrepancies quicker and more efficiently. **What do you dislike about Monte Carlo?** When making a monitor the default adds all checks instantly, If you ever have to change them you have to go remove all of them and add them one by one. **What problems is Monte Carlo solving and how is that benefiting you?** Montecarlo is helping make sure that we provide the best possible data to our stakeholders by allowing us to create custom monitors that give us insight into very specific situations. ### 28. Monte Carlo is the trusted tool our data engineering team relies on to ensure data quality! **Rating:** 5.0/5.0 stars **Reviewed by:** Mariana A. | Team Lead, Data Engineering, Enterprise (> 1000 emp.) **Reviewed Date:** January 24, 2025 **What do you like best about Monte Carlo?** One of the things I really appreciate about Monte Carlo is its automated, out-of-the-box monitors powered by anomaly detection, which learn from our data patterns and alert us to irregularities. It has quickly become an indispensable tool for uncovering unknown data quality issues in our daily operations. **What do you dislike about Monte Carlo?** Monte Carlo is less effective for infrequently updated data, such as bi-weekly, monthly, or quarterly datasets, as the out-of-the-box monitors are not designed to support these use-cases. While custom monitors can address this, they sacrifice scalability, reducing the tool's overall usability for these use cases. **What problems is Monte Carlo solving and how is that benefiting you?** - Detecting and resolving anomalies like missing, duplicated, or corrupted data. - Minimizing periods when data is unreliable or unavailable. - Ensuring schema, volume, and freshness changes do not go unnoticed. - Automating data monitoring across complex, large-scale ecosystems. - Building confidence in data reliability for decision-making. - Providing timely alerts to proactively address data-related incidents. ### 29. Great with a lot of set-up **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Mid-Market (51-1000 emp.) **Reviewed Date:** August 13, 2025 **What do you like best about Monte Carlo?** I think it's awesome at finding issues in our system that we aren't able to capture with our own validation tools. **What do you dislike about Monte Carlo?** It takes a lot of set-up and tuning. We've had to disable a lot of the out of the box monitors for specific assets because they were too noisy. **What problems is Monte Carlo solving and how is that benefiting you?** We collect data from government sites, and it tells us when we haven't collected data. From the alerts, we can investigate if it's an issue on the source's end, within our system, or with replication. ### 30. Interesting product but needs a lot improve **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Internet | Mid-Market (51-1000 emp.) **Reviewed Date:** April 30, 2025 **What do you like best about Monte Carlo?** 1. The result visualization is useful. 2. The customized query is super helpful when we need to design some complicated alerts. 3. The yaml generation function in UI is also helpful so we can make sure the new code can always be in the correct new format. **What do you dislike about Monte Carlo?** 1. ML thresholds do not work well. We are missing lots of important alerts, just because the thresholds go really wide and we are not aware of the issue at all. 2. The MaC keeps on changing, the definition, the structure, the scope, everything keeps on changing frequently, we have to keep on changing our code, which is super annoying. 3. Why do you decide to remove the freshness monitor from MaC and have to let us manually add in a weird other notification place? I do not get the design now. It makes things chaotic. 4. Sometimes dry run passed but after merging the PR, the apply fails. I feel there are some inconsistency there and it is really confusing. **What problems is Monte Carlo solving and how is that benefiting you?** We are able to keep on monitoring some important BQ tables. ### 31. Monte Carlo review 05-20-2025 **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Entertainment | Mid-Market (51-1000 emp.) **Reviewed Date:** May 20, 2025 **What do you like best about Monte Carlo?** Excellent connectors for analysis and monitoring. Lineage is very good and readable. Support is outstanding. API documentation is generally good and the API explorer is nice. Simple to set up monitors and add assets. We are using Monte Carlo extensively already given its ease of use and ability to check for anomalies. **What do you dislike about Monte Carlo?** Adding descriptions to objects like monitors is basically missing. It would be helpful to have a title and description field vs. having a limited description field that acts as the title as well. It is messy and requires too much curation governance. Monitors and Assets should have this capability. Also, the APIs are ok but doc should contain better examples for use. **What problems is Monte Carlo solving and how is that benefiting you?** The ability to track out of range thresholds allows us to catch issues with data faster. We can resolve problems before they impact clients. ### 32. Great tool and concept; needs some added functionality **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Food & Beverages | Enterprise (> 1000 emp.) **Reviewed Date:** January 30, 2025 **What do you like best about Monte Carlo?** I think Monte Carlo is a great way to monitor data issues and I love the "built-in" freshness/volume anomaly monitors on any tables added to Monte Carlo. **What do you dislike about Monte Carlo?** We are using the Monte Carlo product to monitor our BigQuery tables. I have chatted with Monte Carlo support about this before and put in a ticket; but it would be great if we were able to set variables within Monte Carlo monitors (we wanted to use a list within the monitor in order to take advantage of partitioning in BigQuery, as BigQuery does not support dynamic partitioning and thus a CTE would not use partitioning correctly). Scenario: Using a list within a Monte Carlo monitor results in failure. The MC monitor simply takes the first output written in the monitor (the result of setting the list) and considers that as the monitor. The rest of the code in the monitor (after the list is set) is not considered. You can see the ticket or contact me for additional details/explanation. Additionally, I think it would be useful if there were more automated monitors (for example, you could set up an automated monitor so that for ANY anomalous value in the table, the monitor is triggered). **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo is helping alert us to issues with data quality and freshness. It also helps the data scientists on a connected team be alerted to changes in the distribution of consumers we have purchase data for. ### 33. Nice tool with easy api access for developers **Rating:** 3.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** May 14, 2025 **What do you like best about Monte Carlo?** Freshness, volume and validation checks are helpful for users. It has sufficient api's for developers to use Monitor as code. Building dashboards are helpful. **What do you dislike about Monte Carlo?** Completeness checks need to be improved. Alerts with new message after creating incidents is not currently supported. Some of the search option for filters in the UI for assets doesn't work often **What problems is Monte Carlo solving and how is that benefiting you?** Custom monitors are really helpful in finding issues. Freshness monitors detects for any failed data arrival or dag failures. Slack alerts are easy to configure and can be routed to actual team responsible for alerts ### 34. Easy to use with a wide selection of useful defaults **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** April 28, 2025 **What do you like best about Monte Carlo?** How easy it is to setup monitors, the wide selection of default monitors available. I also like how one can comment on alerts and view their history. **What do you dislike about Monte Carlo?** The limitation on number of values we can segment a query by. **What problems is Monte Carlo solving and how is that benefiting you?** Monitoring data quality and preventing issues from manifesting into customer environments. ### 35. Really good to use this tool **Rating:** 4.0/5.0 stars **Reviewed by:** Anderson P. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** November 27, 2024 **What do you like best about Monte Carlo?** The way we can create dinamic reports on top o the tables, and also the ml that learns with the data to generate alerts. **What do you dislike about Monte Carlo?** The notification message on the alerts was not working when I used it. It was a very powefull feature and it was not working. **What problems is Monte Carlo solving and how is that benefiting you?** It solves the problem that we do not have to create several piplelines to monitor the data, with a couple clicks you can put several tables to be monitor. Also the dinamic monitoring. ### 36. Monte Carlo Product Review **Rating:** 2.5/5.0 stars **Reviewed by:** Verified User in Marketing and Advertising | Mid-Market (51-1000 emp.) **Reviewed Date:** April 28, 2025 **What do you like best about Monte Carlo?** UI is great Ease of setting up alerts Data observability focused product **What do you dislike about Monte Carlo?** Automatic threshold algorithm - it is hard to make it work for all possible timeseries Limited automation capabilities **What problems is Monte Carlo solving and how is that benefiting you?** Data quality for BQ tables ## Monte Carlo Discussions - [What is Monte Carlo software?](https://www.g2.com/discussions/what-is-monte-carlo-software) - 1 comment - [View Monte Carlo pricing details and edition comparison](https://www.g2.com/products/monte-carlo/reviews?filters%5Bsentiment_snippet%5D=1037038&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-08-14+21%3A51%3A56+-0500&secure%5Bsession_id%5D=ebc5ff5a-f1f2-427d-8ab7-706aa877e4a6&secure%5Btoken%5D=c5167c86e473225339ed1b446d24fb7ce7bc16229b294ef033ffe8682b71c197&format=llm_user) ## Monte Carlo Integrations - 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[Tableau](https://www.g2.com/products/tableau/reviews) - [Together.ai](https://www.g2.com/products/together-ai/reviews) ## Monte Carlo Features **Functionality** - Monitoring - Alerting - Logging - Response Time - Reporting - Data Visualization - Performance Monitoring - Real-Time Monitoring - Server Monitoring - Real-Time Reporting - Uptime Reporting - Transaction Monitoring - Real-Time Data **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Functionality** - Real-time Analytics - Data quality monitoring - Automation - End to End visiblity **Agentic AI - DataOps Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Decision Making **Tracing & Debugging** - Agent Debugging - Trace Visualization - End-to-End Agent Tracing **Analytics** - Analytics capabilities - Dasboard visualizations **Management** - Anomaly identification - Single pane view - Real-time alerts - Data lineage - Integrations **Agentic AI - Database Monitoring** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations - Capacity Planning **Evaluation & Quality** - Regression Testing - Hallucination Detection - Automated Output Evaluation **Additional Functionality** - Resource Management - Anomaly Detection - Visual Analytics - Remote Monitoring & Management - Secure Data Storage - Dashboard - Generative AI - Configuration Management - API - User Management - Capacity Management - Diagnostic Tools - Dependency Tracking - Troubleshooting - Reporting & Statistics - Reporting/Analytics - Predictive Analytics - Audit Management - Real-Time Notifications - Application Management - Data Storage Management - Application-Level Analysis - Multitenancy - Query Analysis - Performance Management - Access Controls/Permissions - Compliance Management - Historical Trend Analysis - Alerts/Notifications - Summary Reports - AI Copilot - Event Logs - Dashboard Creation - Automated Discovery - Prioritization - Issue Tracking - Activity Dashboard - Performance Metrics - Resource Optimization - Real-Time Analytics - Status Tracking **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation **Production Monitoring** - Alerts & Notifications - Latency Monitoring - Token Usage & Cost Tracking **Functionality** - Identification - Correction - Normalization - Preventative Cleaning - Data Matching - Real-Time Data **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Agentic AI - Data Observability** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Natural Language Interaction - Proactive Assistance **Agent Discovery & Governance** - Audit Logging - Agent Discovery - Policy Compliance Monitoring **Management** - Reporting - Automation - Quality Audits - Dashboard - Governance **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **Additional Functionality** - Metadata Management - Collaboration Tools - Search/Filter - Workflow Management - AI Copilot - Third-Party Integrations - Data Synchronization - Data Import/Export - Customizable Rules - Master Data Management - Monitoring - Data Transformation - Multiple Data Sources - Self Service Portal - Customer Database - Data Verification - Data Migration - Multi-Language - Single Sign On - Duplicate Detection - Email Address Extraction - Reporting/Analytics - Data Profiling - Data Extraction - Data Mapping - Address Validation - Match & Merge - Performance Metrics - Visual Analytics - Version Control - API - Data Capture and Transfer - Access Controls/Permissions - Compliance Management - Data Discovery ## Top Monte Carlo Alternatives - [Acceldata](https://www.g2.com/products/acceldata/reviews) - 4.4/5.0 (55 reviews) - [Anomalo](https://www.g2.com/products/anomalo/reviews) - 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