--- title: Monte Carlo Reviews meta_title: 'Monte Carlo Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 531 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: 531 scale: '5' date_modified: '2026-07-21' 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:** 531 ## 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. Powerful data quality product with a passionate and dedicated team behind it **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Human Resources | Mid-Market (51-1000 emp.) **Reviewed Date:** August 19, 2024 **What do you like best about Monte Carlo?** The Monte Carlo account team has been extremely supportive and dedicated to getting the tool installed and working as best we can. It is simple to create new data quality rules, and has been easy to get started. There are many data quality rule options that allow us to implement a variety of validation rules very easily. **What do you dislike about Monte Carlo?** The billing method of paying per table means that we have to be selective about where we use the tool. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo helps us monitor our data estate in order to detect data quality issues before our customers report them. **Official Response from Sydney Nielsen:** > We're glad to hear that our account team has been supportive. We think they're great! And thanks for sharing your feedback on our pricing. I've shared that with our team for their consideration. Thanks! ### 2. Monte Carlo finds the needle in a haystack **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Online Media | Enterprise (> 1000 emp.) **Reviewed Date:** April 25, 2024 **What do you like best about Monte Carlo?** 1) Ease of set-up - Out-of-the-box monitors for freshness, volume, and schema start training as soon as the MC service principal has access to the tables. 2) Effective Visuals – Results and Incident are visually appealing, but most importantly they present the data in a way that can be easily consumed. The percent increase or decrease for a metric is presented over days or weeks aside the row counts to give context. 3) Seamless Slack integration – It is quick to set-up MC monitor notifications via Slack channels. Responses written in Slack are saved to Monte Carlo, saving time and effort. 4) Low noise - MC keeps the noise level down. False-positive issues are not as common as found in other tools. 5) Customer support - MC hands-down has the best customer support. Their team feels part of our own. They have learned the nuances of our data. They respond quickly to all questions and concerns. Suggestions that we have made about the tool have been implemented (quickly). They are constantly evolving the tool to make it even better. **What do you dislike about Monte Carlo?** We have many custom monitors. We can effectively see and manage these monitors in the Monte Carlo tool. On our wish list, we would also like to see these monitors by schedule time. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo helps us identify bugs and data freshness issues quickly, enabling teams to fix the problems faster. Having a quicker turnaround saves the company money by having less data loss and backfills. Better data equals better decisions. ### 3. Unparalleled out-of-the-box observability and ease of integration **Rating:** 4.5/5.0 stars **Reviewed by:** Tim M. | Enterprise (> 1000 emp.) **Reviewed Date:** April 29, 2024 **What do you like best about Monte Carlo?** The best functionality for me were the out-of-the-box ML monitors on data volume and freshness. These are hard to set up manually and they have proved crucial to use uncovering the "unknown" unknowns issues. **What do you dislike about Monte Carlo?** Some functionality would benefit from more flexibility in the setup. For example, data integration errors and weekly digest emails are always sent, but we would like to adjust if and who receives them. **What problems is Monte Carlo solving and how is that benefiting you?** Low data quality, unreliable data sources. ### 4. Great tool to aid at places you don't look at **Rating:** 4.0/5.0 stars **Reviewed by:** Barak M. | Mid-Market (51-1000 emp.) **Reviewed Date:** March 05, 2023 **What do you like best about Monte Carlo?** Monte Carlo is a great tool to monitor data you don't watch for yourself. It helps you catch changes in the application level that haven't been communicated enough and watch out for external interference. **What do you dislike about Monte Carlo?** The BI insights are lack of details. We would like to have more of them and find more errors at the places where the management looks at reports. We don't get enough details about changed schemas. We get many incidents and don't know which ones are significant or which are not. Adding a significant weight to each one of those changes would be welcome. **What problems is Monte Carlo solving and how is that benefiting you?** It helps us know if the data source got changed (or had issues) that we are unaware of otherwise. ### 5. mc is successful in catching anomalies in terms of freshness and volume except false positive alerts **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Marketing and Advertising | Mid-Market (51-1000 emp.) **Reviewed Date:** April 17, 2024 **What do you like best about Monte Carlo?** Automated monitors can quickly detect the anomalies and announce it via slack alert. **What do you dislike about Monte Carlo?** Let's say an alert is sent to our slack channel about a freshness anomaly for several tables. When the anomaly of one of these tables is solved, but other still remains having the issue, it sends a thread message regarding as the current late amounts of the tables, including the table which has no freshness anomly anymore. However, we expect to the see only the current problematic tables in the thread messages. **What problems is Monte Carlo solving and how is that benefiting you?** it detects the abnormal size changes and the freshness anomalies of our tables. Additionaly, we are informed about the schema changes like a type change of a column in our table. ### 6. It is a great tool for data observability **Rating:** 4.5/5.0 stars **Reviewed by:** Abhishek S. | Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** August 20, 2024 **What do you like best about Monte Carlo?** The UI is very user friendly and intuitive. Ease of Integretion to various channels/sources. **What do you dislike about Monte Carlo?** Few features need to be more stable. For example tagging must be simplified, usage of secondary query engine to test and create monitors etc. **What problems is Monte Carlo solving and how is that benefiting you?** Having a centralized platform for monitoring and managing all the data anamolies is great. **Official Response from Sydney Nielsen:** > We're glad to hear that you find Monte Carlo's UI user-friendly and intuitive. We appreciate your feedback on the features that need improvement, and are constantly working to enhance the stability and functionality of our platform. This feedback has been shared with our Product team. :) ### 7. Great out of the box functionality and insights presented in an accessible way **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Online Media | Enterprise (> 1000 emp.) **Reviewed Date:** April 18, 2024 **What do you like best about Monte Carlo?** Very low bar to entry via easy set up providing large number of insights right out of the box **What do you dislike about Monte Carlo?** Can be difficult to stay on top of alerts and ensure they are being addressed, but is more of an organisational problem **What problems is Monte Carlo solving and how is that benefiting you?** * Enabling fast time to resolution for data issues reducing internal customer complaints on timeliness of dashboards and internal processes * Highlighting and allowing tracing of issues which may never have been spotted or only come to light much later such as unusual traffic patterns / data growth which point to other tech issues which need to be addressed. This can help avoid bad business decision making related to bad data. ### 8. Great combination of data observability and data quality **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Chemicals | Enterprise (> 1000 emp.) **Reviewed Date:** January 25, 2024 **What do you like best about Monte Carlo?** 1. the ease of setting up and understanding the monitors 2. Great variety of audiences and the ability to configure custom messages that includes elements from your actual query 3. The observability part is extremely powerfull. It observed all irregularities that were reported by users in ServiceNow plus an additional set of findings 4. It's good to see that Monte Carlo develops in an iterative way. You see the application continously changing, mostly for the better while the basic functionality stays in plays. **What do you dislike about Monte Carlo?** Monte Carlo is not yet really suited to work with many from multiple teams. People from different teams can see and report on each others monitors, which is good, but it's hard to create with one mouse click an overview of all monitors and incidents for which a particular team is responsible for. We misuse a combination of domain and audience to achieve this plus I appointed myself as Monte Carlo data steward to enforce that users stick to a naming convention for monitors and dashboards **What problems is Monte Carlo solving and how is that benefiting you?** 1. Providing a stable, easy to maintain platform to store and run SQL business rules that have been defined by data stewards working in staff departments in our organization. We report the data quality in a Qlik dashboard 2. Perform pro-active data observability where we want to move away from a situation where our data users report about data issues to a situation where we can pro-actively inform our data users about data issues ### 9. MonteCarlo Usage Survey - WP Engine **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Internet | Enterprise (> 1000 emp.) **Reviewed Date:** January 24, 2024 **What do you like best about Monte Carlo?** The automated monitors that just work out of the box. While we leverage custom monitors quite a bit, having a set of automatically enabled monitors and alerts helps us move fast. MC has also been very easy to integrate into our existing stack. We also use the lineage feature quite a bit whiich measn we are in the platform daily. I also like the constant evolution of the UI and how the improvements always seem to be directed at the most valudable problems. **What do you dislike about Monte Carlo?** There was a hicup in how we managed the transition to the new table model. Hats off you all that you worked with us to find a way to make it work. To offer a suggestion, having things like the usage report available before the decision was made would have been helpful. We had to go through a couple of iterations before we knew for certain what to look at to see our usage against our contract. **What problems is Monte Carlo solving and how is that benefiting you?** We had a lagay data warehouse that was managed and maintained in silos by multiple teams. The primary use for MC was to offer out of the box solutiosn to both observe, identify and respond to data issues in a proactive way. ### 10. Data Observability with Monte Carlo is at a Whole New Level of Excellence **Rating:** 4.5/5.0 stars **Reviewed by:** Asaf Y. | Mid-Market (51-1000 emp.) **Reviewed Date:** January 24, 2024 **What do you like best about Monte Carlo?** Monte Carlo has greatly enhanced our data observability capabilities. It is a user-friendly platform, intuitive, and straightforward to integrate into our existing systems. We've found their customer support to be commendable - quick to respond, helpful, and accommodating. What stands out the most is the commitment to continuous improvement. The team regularly innovates and refines the product, keeping us engaged and optimistic about future enhancements. **What do you dislike about Monte Carlo?** Despite the many positives, there are areas where improvement would be beneficial. Monte Carlo's integration with external tools such as PagerDuty and Opsgenie could be refined to add more functionality, it currently lacks some important features. Moreover, reducing noise in a large data lake could be easier, as it's quite a task at present. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo enhances our data quality and observability, boosting transparency over our myriad of data assets. It alerts us to anomalies, allowing quick action, reducing troubleshooting time, and thus increasing our data's value. ### 11. Monte Carlo has been a game-changer for the whole company in terms of data governance. **Rating:** 4.5/5.0 stars **Reviewed by:** Zsolt H. | Mid-Market (51-1000 emp.) **Reviewed Date:** January 23, 2024 **What do you like best about Monte Carlo?** The table lineage feature makes the Data Engineer and Data Analyst team members workflow much more efficient and the whole data team feels more confident in their knowledge of the data assets. The table metrics enables the realisation of quick actions: we can decide to clean-up unused tables without worrying about unknown consequences or we can focus on optimising the tables based on the importance score. As a Data Engineer, we simply could not have optimosed our costs and improved our platform without the help of Monte Carlo. The customer support is always swift in their response and also our concerns are addressed without hesitation. We have received exceptional support especially from Neil Gleeson, but also from the development team. It was very easy to integrate with our warehouse and we were able to customise the access level quickly and efficiently **What do you dislike about Monte Carlo?** Due to the product being continously developed, we experienced some minor bugs or some features not working as intended in the beginning. These are mostly frontend related bugs, not so distruptive of the core workflows that we use Monte Carlo for. The extensive useage of the product can lead to aleart-fatigue and false positive alrerts that can be distruptive of the day-to-day workflow. We have not been able to properly adjust our workflows to react to MC alerts, there is a high overlap of the MC alerts with our other alerting systems. The false positives - although not comparebale to true positives - were causing some confusion and decreased trust in MC alerts. We could benefit from more customizability: In some cases we have a broken lineage caused by pipelines behaving in ways Monte Carlo cannot detect properly. If we had the option to customise the lineage we could fill these gaps without the need to adjust our pielines or Monte Carlo to build a complex extra feature. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo enables data-driven business teams to increase their supervision over the data that they are working with. This means that they get more insight in how their data is process upstream: they can understand issues quicker and approch the data team with clearer instructions. Setting up alerts helps them react to problems faster and their confidece of the data quality also increases. Since Monte Carlo is user frendly and customisable, they can potentially react to business problems they had not have the capability to monitor before. Another aspect is that Monte Carlo creates an "interface" between business and data teams. Business teams get a better view onto the "data product" that data teams offer, which increases the cooperaation and understanding between the two domains. For data teams Monte Carlo solves also solves other problems, namely the governing capability over the data assets. With hunderds of pipelines and data assets in the thousands, it is easy to lose sight of what assets are important and what is the relation between these assets. The generated lineage graph is a game-changer for data teams which reduces the time to debug and understand certain processes significantly. It also helps optimise the data platform: we can confidently clean up unused assets to save costs and reduce noise. Data teams can also resove issues that previously went undetected which saves many hours spent debugging from both business and tech side. ### 12. Jelena Mataija - Vroom **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Accounting | Mid-Market (51-1000 emp.) **Reviewed Date:** January 22, 2024 **What do you like best about Monte Carlo?** Overall a really nice tool to monitor data quality and validity. Setup is quiet easy enough if you prepare in advance properly, where lovely support comes along and they are always ready to help and to review if needed. Machine learning program can detect if data hasn’t been received on frequent time giving us time to focus on other things and not to monitor our pipelines constantly. As well as slight hints in data quality changes which can come in handy. Customised queries are a nice touch and implementation is endless. For daily data ingestions and quality check this is a nice tool to have since it will alert you on time if anything "fishy" is going on giving you time to focus on other things. If set up correctly there are dashboards that can be shared with different teams throughout the organisation for data monitor and for some internal audits as well. **What do you dislike about Monte Carlo?** You have to have a dedicated people to maintain the monitors and constant update since it is a mechine learning program, meaning you cannot get lazy with it. Ocasionally it can overwellm a database resources but than again it depends on your company organization. **What problems is Monte Carlo solving and how is that benefiting you?** For us currently most used scenarios are ones where we get alert if our data volumes have been changed. If we do not expect it than this is a good signal that our pipelines are late or broke, often those that are coming from third party or API's. Also several custom monitors are in place to track duplicates in our data, fluctuations in group volumes etc. The ones where we check for unique values are really helpful, they can give us a heads up if we need to implement some other changes in our transformation models. ### 13. The overall experience is great. MC keeps the organization data fresh. **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 30, 2024 **What do you like best about Monte Carlo?** Various integrations to multiple platforms Customer support are really helpfull and making an effort to help as much as they can the overall product is easy to use **What do you dislike about Monte Carlo?** can create alert fatigue due to lack of alerts configurations **What problems is Monte Carlo solving and how is that benefiting you?** MC keeps our organization data fresh by monitoring Freshness and Volumes. those alerts can help us find and tune process that can be falty and fix them. we monitor the usage of our data from our customers and see the lineage to the BI tools, a very powerfull tool to investigate which products we deliver are more usefull and which are less usefull ### 14. A Promising Yet Imperfect Tool for Data Observability **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** August 29, 2024 **What do you like best about Monte Carlo?** Pros: Good intentions and potential Simplifies alert systems and visualization **What do you dislike about Monte Carlo?** Cons: ML thresholds are not effective for metrics that vary based on time factors Manual checks/ query implementation are still necessary Uncertain if it is worth the investment **What problems is Monte Carlo solving and how is that benefiting you?** Data quality checks **Official Response from Sydney Nielsen:** > Thank you for your feedback! We appreciate your partnership and are always trying to improve our product to better meet the needs of customers. We'd love to work with you to address your team's concerns. Please reach out to your CSM and we can work with you and your team to ensure you're getting value from the platform. ### 15. Data Monitoring made easy **Rating:** 4.5/5.0 stars **Reviewed by:** Ayodeji O. | Mid-Market (51-1000 emp.) **Reviewed Date:** April 27, 2024 **What do you like best about Monte Carlo?** 1. Monte Carlo pro-actively catches data incidents that would have gone unnoticed 2. Seamless integration with other Data Tools makes it easier to track impact across platforms 3. The documentation provided guides you through the tool 4. The toolkit of checks Monte Carlo can perform makes it the leading tool **What do you dislike about Monte Carlo?** 1. Non-technical users have a bit of a learning curve 2. Out-of-the-box checks could lead to alert fatigue if unchecked **What problems is Monte Carlo solving and how is that benefiting you?** Tracking breaking changes introduced by code (schema changes) Data Irregularities: Data Volume, Update Frequency Custom SQL Checks provide flexibility Keeping other data-related functions in the loop for the data they own ### 16. Good Tool with room for improvement **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** July 25, 2024 **What do you like best about Monte Carlo?** Customizability for alerts and investigations. **What do you dislike about Monte Carlo?** Limit of 500 rows returned for investigation queries. A bit dififcult to navigate and find what im looking for at times **What problems is Monte Carlo solving and how is that benefiting you?** I use Monte Carlo alerts to trigger notificaitons when we are in danger of breaching regulatory requirements for different scenarios. Monte Carlo is use as a final catch for us. ### 17. Monte Carlo is a Game Changer **Rating:** 5.0/5.0 stars **Reviewed by:** Mike C. | Mid-Market (51-1000 emp.) **Reviewed Date:** October 31, 2023 **What do you like best about Monte Carlo?** Monte Carlo is a tool that is easy to implement, use, and drive value through for users across our organization. In our case, we started seeing value during the first week of our proof of concept (POC) period and have continued to generate more value since! A big reason for this is that Monte Carlo not only helps identify a data anomaly faster than we had ben able to before, but also quickly enables users to understand the context and impact of the event. Lineage, Impact Analysis, Correlation Analysis, and integrations with various tools helps you put the alert into context and also resolve the issue much faster than an out of context alert. Outside of the abilities of the core product, the support team is top notch and actively takes feedback to enhance the product. The team is truly customer first. We have never had a customer support request fall off the radar and they are always proactive in making sure we find the best solution for our needs. Lastly, Monte Carlo is a tool designed for scale and the product is centered around making it easy for an organization to standup up a data quality program (with support activities) all through the tool. **What do you dislike about Monte Carlo?** I think there are still some enhancements actively being made to help users more easily administer the tool, but overall, there are not any big dislikes about Monte Carlo. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo has given us end to end observaiblity of key data pipelines that power the core of our business. Data trust is a key factor in any analysis and Monte Carlo has increasingly enabled us to trust our data and quickly take action when things begin to look off. ### 18. Great tool for data reliability **Rating:** 4.5/5.0 stars **Reviewed by:** Zeng F. | Mid-Market (51-1000 emp.) **Reviewed Date:** January 23, 2024 **What do you like best about Monte Carlo?** Anomaly detection, key asset clarification, and lineage build. We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse. **What do you dislike about Monte Carlo?** Recently we have some false alerts about the deleted tables, but support team helps us fix them quickly **What problems is Monte Carlo solving and how is that benefiting you?** Anomaly detection, key asset clarification, and lineage build. We have a large number of ETL jobs but we don't have a good tool to govern those jobs until we have Monte Carlo. MC allows us to monitor thousands of our tables in the data warehouse. ### 19. A good tool to have **Rating:** 3.5/5.0 stars **Reviewed by:** Gerard C. | Mid-Market (51-1000 emp.) **Reviewed Date:** April 17, 2024 **What do you like best about Monte Carlo?** It helps us to do anomaly detection pretty easily and discover a lot of errors we wouldn't otherwise. **What do you dislike about Monte Carlo?** Sometimes one wishes to have more integrations to have end to end quality tests. It doesn't support crossed soures lineage (data sharing) **What problems is Monte Carlo solving and how is that benefiting you?** * Knowing which assets have data quality issues * Knowing where this quality issues come from * Knowing where the data issue is being propagated ### 20. Seamless and invaluable level of insight observability to focus on enrichment and innovation. **Rating:** 5.0/5.0 stars **Reviewed by:** Zeshan A. | Enterprise (> 1000 emp.) **Reviewed Date:** October 27, 2023 **What do you like best about Monte Carlo?** Being part of the Data Team, MC is getting us timely intel on both edge case issues and more common BAU ones that crop up from time to time. MC alerting identifies what it believes to be out of the ordinary coupled with it's in-built lineage tool reducing our time spent on investigation and quicker resolutions. A direct consequence of this being the number of data related queries from data consumers in our business has significantly dropped. **What do you dislike about Monte Carlo?** There are no dislikes from me on the MC product as a whole and it is evolving so looking forward to whats to come. There is a lot of information available and thats certainly not a bad thing, one tiny nit would be a way to group issues from multiple pipeline streams which are related. **What problems is Monte Carlo solving and how is that benefiting you?** Timely intel for key Data Warehouse tables e.g - Unusually stale tables which can be from 3rd party syncing functions issues - Alerting on duplicate keys in reporting tables - Table schema changes With this sort of visibility we have better control over our data which depends on many different pipelines, consolidating all that reporting and alerting is invaluable. ### 21. MC gained us visibility to our data and made it easier for us to communicate issues with our users **Rating:** 5.0/5.0 stars **Reviewed by:** Dana N. | Mid-Market (51-1000 emp.) **Reviewed Date:** January 24, 2024 **What do you like best about Monte Carlo?** Easy UI, easy integration with other tools like slack/DBT/Tableau. Customer reps are also great- whenever we have a question, I know who to ask and I can reach out to the team directly **What do you dislike about Monte Carlo?** Nothing much- there are a few features that we would like having, but I have flagged these to the product team and it seems like they are looking into it **What problems is Monte Carlo solving and how is that benefiting you?** I am being alerted whenever my data breaks and it is easy to spot the impact of the data issue. Also, communicating the issues with our users is easy- they are part of the slack channel and are being alerted on time ### 22. Monte Carlo the great that observability tool **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Logistics and Supply Chain | Enterprise (> 1000 emp.) **Reviewed Date:** May 01, 2024 **What do you like best about Monte Carlo?** I like the Monte Carlo concept and a working product. The pre-sales and implementation have been very professional and the staff has been very helpul. **What do you dislike about Monte Carlo?** There are no negative aspects about Monte Carlo that I can think of right now. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo is enabling us to foremost build at feedback loop with the product teams who own the data sources so they know straight away if something is wrong with quality of the data they provide. It also enables data scientists to check if there is something wrong with the data in their models. ### 23. Very positive experience **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Manufacturing | Enterprise (> 1000 emp.) **Reviewed Date:** August 26, 2024 **What do you like best about Monte Carlo?** Helped us build a data quality dashboard **What do you dislike about Monte Carlo?** Customisation is still in the early stages. **What problems is Monte Carlo solving and how is that benefiting you?** It is helping us quickly identifying data quality issues. This is benefitting us through quicker and more efficient approach to solving issues. **Official Response from Sydney Nielsen:** > Thanks for the feedback! ### 24. Great data observability service! **Rating:** 4.5/5.0 stars **Reviewed by:** Evyatar L. | Enterprise (> 1000 emp.) **Reviewed Date:** April 30, 2024 **What do you like best about Monte Carlo?** Constant monitoring of our data platform on so many aspects, ease of use and flowy UI. We use it on daily basis since its alerting solution is very versatile and persice. **What do you dislike about Monte Carlo?** In some cases its first AI generated threshold is inadectiute and causes data lags that causes us to miss it out. A human intervension will than resolve this issue. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo solves a variaty of issues such as data integrity, data validation, error history, profound visablilly angles on our asests and amazing lineage that help us with impact analisys. ### 25. MonteCarlo is a critical piece of data monitoring **Rating:** 5.0/5.0 stars **Reviewed by:** Scott S. | Senior Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 21, 2024 **What do you like best about Monte Carlo?** Automatic adding of new tables Built-in monitors (latency, deletions, schema change) Table & column lineage **What do you dislike about Monte Carlo?** Value anomoly detection monitoring is too noisy. It's tough to get right. **What problems is Monte Carlo solving and how is that benefiting you?** Primarily monitoring latency automatically in our warehouse **Official Response from Sydney Nielsen:** > Thanks for the feedback! We're thrilled to hear that you find the automatic addition of new tables and the built-in monitors valuable. We understand your frustration with the noisy alerts and will work to improve this feature for a better user experience. ### 26. We are working with MonteCarlo to monitor the data health of our DWH platform. **Rating:** 4.5/5.0 stars **Reviewed by:** Dov Z. | Mid-Market (51-1000 emp.) **Reviewed Date:** January 24, 2024 **What do you like best about Monte Carlo?** Simple integration with data platform stack (in our case Snowflake,DBT,Looker) Easy to use, no advanced technical skills required Ability to monitor schema changes Great catalog abilities (Lineage,search) A single view of all data issues across the entire pipeline (DB to Looker) Customer success and support are very responsive Integration with Slack **What do you dislike about Monte Carlo?** There is no option to save data errors in the database (only statistics and sample data can be obtained). **What problems is Monte Carlo solving and how is that benefiting you?** Finding data issues in the DWH before they affect business users impact analysis of data issues (using end to end lineage) Monitoring DBT jobs Schema changes (of raw data) alerts ### 27. Great data monitoring tool **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Automotive | Enterprise (> 1000 emp.) **Reviewed Date:** August 26, 2024 **What do you like best about Monte Carlo?** I like the flexibility it gives to monitor data and set custom rules via coding **What do you dislike about Monte Carlo?** Hard to integrate with SAP directly..... **What problems is Monte Carlo solving and how is that benefiting you?** Identifying data quality issues based on business rules **Official Response from Sydney Nielsen:** > Thank you for sharing your feedback about the integration with SAP. We're continuously working to improve our integrations. Your feedback is noted :) ### 28. Create an automated proactive fail-safe and data trust using the Monte Carlo Data Observability Platform. **Rating:** 5.0/5.0 stars **Reviewed by:** Vishal S. | Solutions & Deployment Architect, Enterprise (> 1000 emp.) **Reviewed Date:** March 15, 2023 **What do you like best about Monte Carlo?** Get Value for Money (ROI) on investment immediately Generate Data Trust Very Easy to Implement Rich Integration with Tools **What do you dislike about Monte Carlo?** Only available for SAAS Platforms and Dashboard Tools Workflow Access Control is not yet available **What problems is Monte Carlo solving and how is that benefiting you?** Proactive Data Monitoring Certifying Data - Data Trust Data Lineage to show the full impact of Anomaly ### 29. Alerts on the go **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** July 25, 2024 **What do you like best about Monte Carlo?** The ease of getting alerts to operations for quick needs / pivots **What do you dislike about Monte Carlo?** I haven't seen any yet - UI is pretty good and straight forward. **What problems is Monte Carlo solving and how is that benefiting you?** We are trying to be compliant and built alerts to ensure that we are alerted of near breaches ### 30. Montecarlo improved availability with better SLA **Rating:** 4.5/5.0 stars **Reviewed by:** Kalyan S. | Enterprise (> 1000 emp.) **Reviewed Date:** April 17, 2024 **What do you like best about Monte Carlo?** Montecarlo seamlessly integrates with database and Integration tools to bring imformation and incedents in single place. Very easy to monitor end to end data flow to provide quality data to users. Montecarlo customer support is excellent. **What do you dislike about Monte Carlo?** 1. not able to import or export table list to excel. 2. not able to select tables while onboarding to MC. every table is imported **What problems is Monte Carlo solving and how is that benefiting you?** we are able to identify and data anomolies better like volume change/schema change/freshness. ability to create tickets and track the progress in one place is very useful. ### 31. Monte Carlo is an awesome toll for Data Quality and Observability **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Sports | Enterprise (> 1000 emp.) **Reviewed Date:** April 17, 2024 **What do you like best about Monte Carlo?** Its an great tool to define the DQ rules The Dahsboards are very intutive and easy to track incidents Freshness & volume Monitors are very helpful its userfriendy UI makes it easy to use the tool The seamless integration with other techstack **What do you dislike about Monte Carlo?** The time takes to trains the models is bit **What problems is Monte Carlo solving and how is that benefiting you?** In Traditional Data Quality setup is very difficult to keep track to all DQ issues at once. Monte Carlo is helping to have all DQ & Data Observibility at one place with easy to use UI interface ### 32. Doing a Great Job of Monitoring **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consumer Services | Enterprise (> 1000 emp.) **Reviewed Date:** August 14, 2024 **What do you like best about Monte Carlo?** It provides a user friendly was to monitor our enviroment **What do you dislike about Monte Carlo?** Have not run into any issues at the moment **What problems is Monte Carlo solving and how is that benefiting you?** It has helped with monitoring an disscrepencies in our processes **Official Response from Sydney Nielsen:** > We appreciate your feedback! We're glad to hear that Monte Carlo is providing a user-friendly way to monitor your environment. ### 33. Monte Carlo Review - HubSpot Analytics Engineering POV **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Internet | Enterprise (> 1000 emp.) **Reviewed Date:** August 01, 2024 **What do you like best about Monte Carlo?** * automatic set up on volume and anomaly detection **What do you dislike about Monte Carlo?** * inflexible nature in variable usage (e.g. capitalization of table names) **What problems is Monte Carlo solving and how is that benefiting you?** Data integrity to ensure assets have expected volume & freshness as they get produced daily. ### 34. Our data quality watchdog **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Telecommunications | Mid-Market (51-1000 emp.) **Reviewed Date:** April 29, 2024 **What do you like best about Monte Carlo?** The ability to swiftly detect unexpected behavior in our data tables. This real-time alerting capability provides invaluable peace of mind, allowing us, data-scientists, to proactively address anomalies before they escalate into larger issues. **What do you dislike about Monte Carlo?** One aspect that could be improved is the occasional occurrence of false positives. While these are to be expected to some degree in any alerting system, reducing their frequency would make the overall user experience almost perfect. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo helps us solve our big problem: making sure our data is trustworthy every day. It keeps an eye on our data tables all the time and alerts us if something doesn't seem right. This helps us keep our data reliable and make smart decisions confidently. ### 35. Perfect Data observality tools. **Rating:** 5.0/5.0 stars **Reviewed by:** Jivan T. | Data Developer (Engineering), Mid-Market (51-1000 emp.) **Reviewed Date:** May 28, 2024 **What do you like best about Monte Carlo?** This is best Data observality tool which helps to represent correct data in all our dashboards through it anomalies. **What do you dislike about Monte Carlo?** It's UI is little confusing at the initial stage. **What problems is Monte Carlo solving and how is that benefiting you?** It solving the issue of correct data in datalake. Which alternative make sure the perfect data in all the dashboard and multiple data science models. ### 36. Great tool for Data Governance with space for improvements **Rating:** 4.5/5.0 stars **Reviewed by:** Leticia L. | Mid-Market (51-1000 emp.) **Reviewed Date:** January 25, 2024 **What do you like best about Monte Carlo?** I really like the integration with airflow, allowing to identify tasks that load specific tables. The monitors are also very useful and the possibility of identifying domains and personalize the alerts for each of them. The customer support is really good and fast. **What do you dislike about Monte Carlo?** I am not so confident about the field lineage, specially when copying data, we have faced sometimes the disruption of the lineage due to that. **What problems is Monte Carlo solving and how is that benefiting you?** Is spotting any issue with data really fast allowing us to fix some problems before business even noticing that. ### 37. Best in class data quality tool **Rating:** 4.5/5.0 stars **Reviewed by:** Josep A. | Mid-Market (51-1000 emp.) **Reviewed Date:** April 22, 2024 **What do you like best about Monte Carlo?** Monte Carlo default monitors make sure you don't miss any anomaly in your data. Specially of help are that they also work with field segmentation. **What do you dislike about Monte Carlo?** The only way to integrate with Monte Carlo right now is through Redshift. If there was an API/SDK so we could send data or sampled data we would be able to monitor other data sources such as Kafka. **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo allows us to detect and prevent problems with our data because of problems in our infrastructure. ### 38. OverAll Good. But, Still needs to be improved a lot. **Rating:** 3.0/5.0 stars **Reviewed by:** Verified User in Program Development | Small-Business (50 or fewer emp.) **Reviewed Date:** August 02, 2024 **What do you like best about Monte Carlo?** Visualisation and capturings are looks good **What do you dislike about Monte Carlo?** We see some limitations still to add all the filters **What problems is Monte Carlo solving and how is that benefiting you?** Monitoring the data based on different dimentions i.e. Volume and recency of the data **Official Response from Sydney Nielsen:** > Thank you for your feedback. We are glad to hear that you're finding value in the visualizations. We'd love to hear a bit more about your feedback on the filter limitations. If you'd like to share some additional thoughts, please reach out to your Customer Success Manager and we'll get you connected with our Product team. Thanks! ### 39. Great Tool for Data Observability **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Retail | Enterprise (> 1000 emp.) **Reviewed Date:** April 26, 2024 **What do you like best about Monte Carlo?** The incident feature is what I like the best. Its ability to provide real-time alerts for anomalies or issues in data ensures data reliability and integrity. This proactive approach saves valuable time and resources by quickly identifying and resolving issues before they impact operations. **What do you dislike about Monte Carlo?** The UI could be more intuitive and user-friendly. It sometimes feels a bit cluttered and could benefit from a cleaner design to enhance the overall user experience. **What problems is Monte Carlo solving and how is that benefiting you?** Its real-time anomaly detection saves me time. ### 40. Monte Carlo Review **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Publishing | Enterprise (> 1000 emp.) **Reviewed Date:** April 26, 2024 **What do you like best about Monte Carlo?** - Easy to get an end-to-end view of data flows in our warehouse - Great on boarding support - Each user in our company can use it - New features appearing - Some support for Data products **What do you dislike about Monte Carlo?** - Not completely clear how to use it with DBT and some other tools. - API docs HTML page causes browser to freeze because they're too long **What problems is Monte Carlo solving and how is that benefiting you?** Giving me end-to-end observability in BigQuery including simple lineage and column level lineage ### 41. Data Observability Platform that Delivers **Rating:** 4.0/5.0 stars **Reviewed by:** Eli G. | Senior Director of Data Engineering, Small-Business (50 or fewer emp.) **Reviewed Date:** January 24, 2024 **What do you like best about Monte Carlo?** Getting value with minimal cusom work Ease of Integration and implementation Customer support team are great and always assisting and answering our inquiries **What do you dislike about Monte Carlo?** Need a bit more controls over the ML model sensitivity to reduce manual work of creating custom freshness/volume monitors Would appriciate a bit more ease of use on the platform management capabilties (e.g. managing dataset and table monitoring) **What problems is Monte Carlo solving and how is that benefiting you?** Moving our data engineering team to be much more proactive when it comes to finding and fixing data and data pipelines related issues ### 42. Monte Carlo provide reliable and accurate data observability to our data stack. **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** January 28, 2024 **What do you like best about Monte Carlo?** I like the most about Monte Carlo is the Automated Monitoring functionality. Because it remove the hassles of need to create monitor for each tables, by automatically applied the Freshness, Volume and Schema change monitor to any new table imported to our database. **What do you dislike about Monte Carlo?** Most dislike is its UI. I feel that the UI is not inituitive. Eg; **What problems is Monte Carlo solving and how is that benefiting you?** Monte Carlo help to automates thousand of tables in our databases. It provide a consistent observability and come with multiple monitoring option. Plus we can integrate Monte Carlo to our Slack channel to provide alert to our team. With Monte Carlo, we dont need manually create a custom SQL script for our tables, as Monte Carlo helps to automate those task ### 43. The best Data observability tool for the data quality monitoring. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Mid-Market (51-1000 emp.) **Reviewed Date:** May 01, 2024 **What do you like best about Monte Carlo?** It provide centralised view for all your important tables using schema, lineage, freshness, volume anomalies so that we make sure correct and updated data is reflecting in dashboards. it provide Custom as well as machine generated rules. **What do you dislike about Monte Carlo?** The UI is little confusing at the starting. **What problems is Monte Carlo solving and how is that benefiting you?** Accuracy of the data that we are ingesting and going to use in different application. understand the which part of the pipeline is broken or slow. ### 44. Best Data Observability Platform **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Mid-Market (51-1000 emp.) **Reviewed Date:** January 25, 2024 **What do you like best about Monte Carlo?** i like the way how MonteCarlo provides standard out-of-the box monitors, simple -no frills - UI to setup, the way how monitors, alerts are integrated with Slack, Collaboration from Product team on day to day basis in slack, promptness in response, knowledgable product managers & periodic sync up with them, Incident management, data sharing **What do you dislike about Monte Carlo?** There is a period initially when we got bogged down by alert fatigue & few monitor setup options that were maturing. Those have been eliminated as of now **What problems is Monte Carlo solving and how is that benefiting you?** near real-time Data Quality checks on the data, immediate alerting mechanisms in Colloboration channel, ability to create domains & have business users self-service - without losing the overall observability & control on the environment. ### 45. Amazing tool for data observation and creation of different monitors as per tables behavious **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Oil & Energy | Enterprise (> 1000 emp.) **Reviewed Date:** May 01, 2024 **What do you like best about Monte Carlo?** Amazing features availble to create monitors using UI direct **What do you dislike about Monte Carlo?** Its frequency to update table data according to actual table is too slow, It can be increased **What problems is Monte Carlo solving and how is that benefiting you?** We were facing issue for Data science job as there is change in source database and due to this table data/schema was changing so using this tool we able to create custom monitors which alerts us and we makes changes accordingly ### 46. MC - easy to use tool for monitoring data quality **Rating:** 4.5/5.0 stars **Reviewed by:** Asaf E. | Mid-Market (51-1000 emp.) **Reviewed Date:** January 24, 2024 **What do you like best about Monte Carlo?** Easy to use as a day to day monitoring tool. Great customer support, very responsive and always adding effective features. **What do you dislike about Monte Carlo?** It does not easily output all erronous rows, the default is a sample. There is no easy connection between one incident to the other. **What problems is Monte Carlo solving and how is that benefiting you?** Our main issue is a daily feed of data from our partners which has a ton of errors. We set many monitors in MC, which alert us each day on new data incidents. ### 47. Monte Carlo personnel experience **Rating:** 5.0/5.0 stars **Reviewed by:** Desh R. | Mid-Market (51-1000 emp.) **Reviewed Date:** April 25, 2024 **What do you like best about Monte Carlo?** Monte Carlo is user friendly and of great utility when table monitoring is needed. **What do you dislike about Monte Carlo?** Past related anomalies need to be simpler to view. **What problems is Monte Carlo solving and how is that benefiting you?** Easily detect bad data before the jobs fails. ### 48. Monte Carlo is awesome **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** July 29, 2024 **What do you like best about Monte Carlo?** Automatic data anomaly detection. Data Lineage. Excellent customer support. **What do you dislike about Monte Carlo?** Automatic data anomaly detection can be noisy when many issues are happening at the same time. **What problems is Monte Carlo solving and how is that benefiting you?** The biggest problem that Monte Carlo solve for us is its Data Lineage feature which helped us tracked down data depedencies. We also use the custom monitor to detect specific data issues. ### 49. Great and useful data monitoring system **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** April 30, 2024 **What do you like best about Monte Carlo?** I like the fact you can monitor different tables and views, see the lineage of your tables. have built in monitors like data freshness and volume, but also the ability to add custom tests. **What do you dislike about Monte Carlo?** I would like better fitted integration to email. sometimes you get false positives. **What problems is Monte Carlo solving and how is that benefiting you?** -monitoring data quality -finding issues in the data fast -being able to add your own tests. ### 50. Easy to use **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Retail | Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2024 **What do you like best about Monte Carlo?** Easy to use and understand. The time it taskes to develop a monitor is low. **What do you dislike about Monte Carlo?** It might be tricky to implement complex logic while building alerting **What problems is Monte Carlo solving and how is that benefiting you?** Helping us catch data quality issues ## 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?page=8&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-07-21+16%3A07%3A35+-0500&secure%5Bsession_id%5D=50f2f1d4-473e-4871-90c0-18ec20379c87&secure%5Btoken%5D=c916cdf7782eb09acc772c6aa7c847a230af4e1392fa1a3219cc513cd5e2960d&format=llm_user) ## Monte Carlo Integrations - [Alation](https://www.g2.com/products/alation/reviews) - [Amazon Athena](https://www.g2.com/products/amazon-athena/reviews) - [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews) - [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews) - [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews) - [Atlan](https://www.g2.com/products/atlan/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews) - [Coalesce Catalog (formerly CastorDoc)](https://www.g2.com/products/castor-doc/reviews) - [Collibra](https://www.g2.com/products/collibra/reviews) - [Databricks](https://www.g2.com/products/databricks/reviews) - [dbt](https://www.g2.com/products/dbt/reviews) - [dbt + Tableau](https://www.g2.com/products/dbt-tableau/reviews) - [Fivetran](https://www.g2.com/products/fivetran/reviews) - [Git](https://www.g2.com/products/git/reviews) - [GitHub](https://www.g2.com/products/github/reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Hex](https://www.g2.com/products/hex-tech-hex/reviews) - [Jira](https://www.g2.com/products/jira/reviews) - [Looker](https://www.g2.com/products/looker/reviews) - 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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 **Evaluation & Quality** - Regression Testing - Hallucination Detection - Automated Output Evaluation **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 **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 ## 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) - 4.4/5.0 (44 reviews) - [Datadog](https://www.g2.com/products/datadog/reviews) - 4.4/5.0 (714 reviews)