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Monte Carlo vs Splunk Observability Cloud comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
6.9
Monte Carlo accelerates data issue detection by 60%-70% and reduces downtime by 40%-50%, saving 1,200 hours annually.
Sentiment score
6.5
Splunk Observability Cloud boosts efficiency, reduces costs, and enhances productivity with centralized tools and improved monitoring capabilities.
It definitely reduces resource hours needed for work, lessening the effort required significantly compared to when Monte Carlo is not in place.
Data Engineer & Management & Governance Senior Analyst at a tech vendor with 10,001+ employees
Monte Carlo has solved the challenge of monitoring ingestion health at scale.
Project Superintendent at Teshama Group
Monte Carlo saves me roughly 30% to 40% of my time in doing verifications or data quality checks.
Enterprise Network Architect at Concordia University-Wisconsin
We have saved considerable amounts of money, reducing our expenditures from around three to four crores to approximately one to one point two crores.
Senior Manager at Agriculture Skill Council of India
We have been able to save a great deal of money, and our profits have increased by twenty percent.
Project Manager at AGRICULTURE SKILL COUNCIL OF INDIA (ASCI)
Using Splunk has saved my organization about 30% of our budget compared to using multiple different monitoring products.
Senior Manager at Bank of America
 

Customer Service

Sentiment score
6.2
Monte Carlo's customer service is highly rated for providing responsive and efficient support through a team and AI platform.
Sentiment score
7.2
Splunk Observability Cloud's customer service is highly rated for its responsiveness, support, and effective issue resolution.
When I requested help regarding the deletion of monitors, I received a very good and quick response.
Data Engineer & Management & Governance Senior Analyst at a tech vendor with 10,001+ employees
Monte Carlo's customer support team responds very fast.
Staff Data Engineer at a media company with 5,001-10,000 employees
My experiences reaching out to them show that they were very quick to help and very professional.
Project Superintendent at Teshama Group
On a scale of 1 to 10, the customer service and technical support deserve a 10.
Systems Administrator at a insurance company with 1,001-5,000 employees
They have consistently helped us resolve any issues we've encountered.
Software Engineer at UKG
The customer support system is the foundational pillar of any successful business.
Project Manager at AGRICULTURE SKILL COUNCIL OF INDIA (ASCI)
 

Scalability Issues

Sentiment score
7.4
Monte Carlo scales effectively, accommodating increased data demands and providing flexibility for organizations experiencing growth and expanding data volumes.
Sentiment score
6.9
Splunk Observability Cloud is scalable and flexible but can incur high costs; management of custom metrics may be challenging.
Monte Carlo's scalability is impressive.
Data Engineer & Management & Governance Senior Analyst at a tech vendor with 10,001+ employees
As our company's business grows and the data volume increases, Monte Carlo scales very well.
Staff Data Engineer at a media company with 5,001-10,000 employees
Monte Carlo is robust and scalable for our data needs.
Senior Data & Platforms Engineer at PepsiCo
We've used the solution across more than 250 people, including engineers.
Splunk Observability Expert
As we are a growing company transitioning all our applications to the cloud, and with the increasing number of cloud-native applications, Splunk Observability Cloud will help us achieve digital resiliency and reduce our mean time to resolution.
Application Developer at UMB Financial
We have never seen any kind of downtime or crashes, as it has been absolutely very easy to scale.
Project Manager at AGRICULTURE SKILL COUNCIL OF INDIA (ASCI)
 

Stability Issues

Sentiment score
8.7
Users praise Monte Carlo for its stable and reliable performance, noting its consistent uptime and absence of crashes.
Sentiment score
7.7
Splunk Observability Cloud is stable, reliable, and scalable, with minor performance issues and occasional but limited downtime.
I did not see any issues with respect to stability.
Principal Data Engineer at Teradata Corporation
When downtime occurs, it raises concerns about how we measure and receive alerts, as everything needs to be in place.
Aws Dev Ops Engineer at a consultancy with 10,001+ employees
Splunk Observability Cloud is very stable.
Software Engineer at Titans Lab
It is highly scalable because it can handle approximately up to one hundred applications at a time without any lapse or lag.
Project Manager at AGRICULTURE SKILL COUNCIL OF INDIA (ASCI)
 

Room For Improvement

Monte Carlo struggles with AI accuracy, user experience, anomaly detection, UI, monitor deletion, database features, and pricing competitiveness.
Splunk Observability Cloud needs better cost transparency, user interface, third-party integration, and improved setup, onboarding, and AI capabilities.
Artificial intelligence can access multiple systems underneath Monte Carlo, such as any kind of database or any kind of real-time source systems.
Principal Data Engineer at Teradata Corporation
Monte Carlo has just updated the UI. The previous one was user-friendly, and now they have added AI-related elements in the current UI, which is good.
Data Engineer & Management & Governance Senior Analyst at a tech vendor with 10,001+ employees
They need to find their way back, establish a product roadmap, and have real engineers work on improvements rather than heavily push AI down users' throats.
Senior Data & Platforms Engineer at PepsiCo
The out-of-the-box customizable dashboards in Splunk Observability Cloud are very effective in showcasing IT performance to business leaders.
IT Operations Engineer at ABC Supply Co. Inc.
The next release of Splunk Observability Cloud should include a feature that makes it so that when looking at charts and dashboards, and also looking at one environment regardless of the product feature that you're in, APM, infrastructure, RUM, the environment that is chosen in the first location when you sign into Splunk Observability Cloud needs to stay persistent all the way through.
Systems Monitoring Engineer II at a government with 10,001+ employees
There should be a solution to update OTeL agents from Splunk Observability Cloud itself.
Senior Software Engineer at WorldPay US
 

Setup Cost

Monte Carlo offers reasonable pricing for enterprise observability, with manageable setup costs and adaptable licensing for different organization sizes.
Enterprise users find Splunk Observability Cloud pricey compared to competitors, but negotiations can reduce costs by 10-15%.
I find it highly affordable for any organization sizes.
Project Superintendent at Teshama Group
Splunk is a bit expensive since it charges based on the indexing rate of data.
Senior Manager at Bank of America
It is expensive, especially when there are other vendors that offer something similar for much cheaper.
Solutions Architect at Ikusi
I can confidently say our availability improved by forty percent, and downtime was reduced by approximately seventy to eighty percent.
Splunk Engineer at Data Elicit Solutions Pvt. Ltd.
 

Valuable Features

Monte Carlo enhances data reliability through AI-driven alerts, anomaly detection, and integration, reducing manual effort and improving decision-making.
Splunk Observability Cloud offers real-time monitoring, AI analytics, and easy integration, enhancing user experience and operational performance.
Monte Carlo has accelerated the development process and has reduced the testing time significantly.
AI Machine Learning Engineer at a tech vendor with 10,001+ employees
The system does not send false alerts.
Principal Data Engineer at Teradata Corporation
Monte Carlo has positively impacted my organization by significantly reducing manual tasks.
Data Engineer & Management & Governance Senior Analyst at a tech vendor with 10,001+ employees
Splunk provides advanced notifications of roadblocks in the application, which helps us to improve and avoid impacts during high-volume days.
Senior Manager at Bank of America
For troubleshooting, we can detect problems in seconds, which is particularly helpful for digital teams.
Splunk Observability Expert
It offers unified visibility for logs, metrics, and traces.
Administrator at a tech vendor with 10,001+ employees
 

Categories and Ranking

Monte Carlo
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
8
Ranking in other categories
Data Quality (23rd), Data Observability (1st)
Splunk Observability Cloud
Average Rating
8.2
Reviews Sentiment
6.8
Number of Reviews
88
Ranking in other categories
Application Performance Monitoring (APM) and Observability (6th), Network Monitoring Software (7th), IT Infrastructure Monitoring (7th), Cloud Monitoring Software (5th), Container Management (6th), Digital Experience Monitoring (DEM) (3rd)
 

Mindshare comparison

Monte Carlo and Splunk Observability Cloud aren’t in the same category and serve different purposes. Monte Carlo is designed for Data Observability and holds a mindshare of 24.4%, down 32.2% compared to last year.
Splunk Observability Cloud, on the other hand, focuses on Application Performance Monitoring (APM) and Observability, holds 2.3% mindshare, up 1.8% since last year.
Data Observability Mindshare Distribution
ProductMindshare (%)
Monte Carlo24.4%
Unravel Data13.8%
Acceldata11.1%
Other50.699999999999996%
Data Observability
Application Performance Monitoring (APM) and Observability Mindshare Distribution
ProductMindshare (%)
Splunk Observability Cloud2.3%
Dynatrace5.3%
Datadog4.6%
Other87.8%
Application Performance Monitoring (APM) and Observability
 

Featured Reviews

KB
Senior Data & Platforms Engineer at PepsiCo
Improved data health and incident reduction have revealed issues while AI direction still needs work
Monte Carlo needs to stop their reliance on AI, as it is not going well and is degrading the entire product. They need to find their way back, establish a product roadmap, and have real engineers work on improvements rather than heavily push AI down users' throats. They need to stop relying on AI as heavily as they have been doing, as this has really degraded the user experience. The overall direction they are taking with AI needs to be examined, as at some point it seems they have simply stopped making any improvements. We have not used Monte Carlo's AI capabilities significantly. We primarily use it for investigating alerts from time to time. However, we do not use it extensively, so I do not think it is fair to comment comprehensively on it. Their incident tracking and incident debugging bot is useful for new analysts who are starting onboard. It helps them debug incidents, get a clearer picture, and achieve a clear head start to reach the root of the problem faster. Regarding accuracy and reliability, I would rate it at eighty to eighty-five percent. Given the current inherent non-reliability of AI models, every single thing that Monte Carlo says needs to be validated.
PK
Project Manager at AGRICULTURE SKILL COUNCIL OF INDIA (ASCI)
Unified observability has improved real-time governance and now drives data-led decisions
Log Observer Connect is embedded here, but we are facing some delays in centralized log collection and analysis, which can be further fastened. We are collecting all the data metrics and decision-making insights, but all these data-driven decisions coming from different applications are not connected somewhere. A consolidated form or correlation of these insights is not happening between each other due to which we feel we are missing something significant. Some generalized feedback includes that predictive alerts or alarms which can be integrated with AI-driven alarms and alerting features should be established so that there is AI-driven intelligence and anomaly detection happening with a complete systematic process in service delivery. Application dependencies are huge, and business and operational dashboards should be improved. Right now there are very interactive custom dashboards, and every now and then, the personalization of enhancements keeps happening. KPI monitoring, executive reporting, and analytics have definitely been introduced to a great extent. There are few things in cloud-native monitoring, such as integration with AWS and Azure, where we sometimes do face lags. Those things can definitely be improved upon. I have used Datadog and Dynatrace before using Splunk Observability Cloud. Datadog was definitely recommended by most of our peers because of its very strong comprehensive observability and very strong and unique dashboard systems. Dynatrace was also very good because they have offered a lot of AI-driven analysis methods and processes, which was helping our organization a lot. Since our organization has a very strong IT ecosystem for agriculture, very different kinds of customized things are required.
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Top Industries

By visitors reading reviews
Financial Services Firm
10%
Computer Software Company
8%
Construction Company
7%
Retailer
7%
Financial Services Firm
13%
Manufacturing Company
9%
Computer Software Company
8%
Construction Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business1
Midsize Enterprise3
Large Enterprise9
By reviewers
Company SizeCount
Small Business32
Midsize Enterprise8
Large Enterprise55
 

Questions from the Community

What is your experience regarding pricing and costs for Monte Carlo?
My experience with pricing, setup costs, and licensing is limited as that falls under the management team's responsibility.
What needs improvement with Monte Carlo?
One way Monte Carlo can be improved is when rules are breached, it sends an email containing alerts. However, if I want to analyze a particular alert deeper, I have to click on the alert link and f...
What is your primary use case for Monte Carlo?
Monte Carlo's main use case is setting rules to test the quality of data coming from the source side. For example, a rule can be set up for null checks in a particular column of source tables. If a...
What needs improvement with SignalFx?
Regarding dashboard customization, while Splunk has many dashboard building options, customers sometimes need to create specific dashboards, particularly for applicative metrics such as Java and pr...
What is your primary use case for SignalFx?
The solution involves observability in general, such as Application Performance Monitoring, and generally addresses digital applications, web applications, sites, and mobile applications. I worked ...
What advice do you have for others considering SignalFx?
We're a customer and end-user. Currently, in France, we cannot use the artificial intelligence option. While this option is enabled for the United States and many countries, it's not yet available ...
 

Also Known As

No data available
Splunk Infrastructure Monitoring, Splunk Real User Monitoring (RUM), Splunk Synthetic Monitoring
 

Overview

 

Sample Customers

Information Not Available
Sunrun, Yelp, Onshape, Tapjoy, Symphony Commerce, Chairish, Clever, Grovo, Bazaar Voice, Zenefits, Avalara
Find out what your peers are saying about Monte Carlo, Informatica, Unravel Data and others in Data Observability. Updated: May 2026.
900,747 professionals have used our research since 2012.