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Datadog vs Monte Carlo 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
6.4
Datadog improves efficiency by reducing response time, optimizing resources, enhancing reliability, and saving costs through better infrastructure monitoring.
Sentiment score
6.9
Monte Carlo accelerates data issue detection by 60%-70% and reduces downtime by 40%-50%, saving 1,200 hours annually.
Previously we had thirteen contractors doing the monitoring for us, which is now reduced to only five.
IT Manager at Liberty Mutual Insurance
Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization.
Sr. Cloud Infrastructure Engineer at a tech vendor with 51-200 employees
We have also seen fewer escalations for minor issues because alerts help us catch problems earlier, which indirectly reduces downtime and improves overall efficiency.
Network Security Consultant at NTT DATA
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
 

Customer Service

Sentiment score
6.7
Datadog's customer service is generally reliable and efficient, with recent improvements noted, despite occasional delays and communication issues.
Sentiment score
6.2
Monte Carlo's customer service is highly rated for providing responsive and efficient support through a team and AI platform.
When I have additional questions, the ticket is updated with actual recommendations or suggestions pointing me in the correct direction.
Applications Web Services Technical Engineer at Ace Hardware
Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional.
Systems Administrator at Townsquare Interactive
I've had a couple instances where I reached out to Datadog's support team, and they have been really super helpful and very kind, even reaching back out after resolving my issues to check if everything's going well.
Security Engineer at Invitation Homes
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
 

Scalability Issues

Sentiment score
7.5
Datadog excels in scalable performance and integration but requires careful ingestion cost management as environments grow.
Sentiment score
7.4
Monte Carlo scales effectively, accommodating increased data demands and providing flexibility for organizations experiencing growth and expanding data volumes.
Datadog's scalability has been great as it has been able to grow with our needs.
IT Manager at Liberty Mutual Insurance
Since it is a SaaS platform, we did not have to worry about backend scaling.
Network Security Consultant at NTT DATA
We have not faced any major performance issues from the platform side; it handles increased metrics and monitoring loads smoothly.
Cyber Security Consultant at HR Software Solution
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
 

Stability Issues

Sentiment score
8.0
Datadog is praised for stability and reliability, with rare, quickly-resolved issues, especially during peak traffic periods.
Sentiment score
8.7
Users praise Monte Carlo for its stable and reliable performance, noting its consistent uptime and absence of crashes.
Metrics collection and alerting have been consistent in day-to-day use.
Cyber Security Consultant at HR Software Solution
Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time.
Security Engineer at Invitation Homes
Datadog seems stable in my experience without any downtime or reliability issues.
Full Stack Developer at Townsquare Interactive
I did not see any issues with respect to stability.
Principal Data Engineer at Teradata Corporation
 

Room For Improvement

Datadog needs better alert management, cost control, data representation, API consistency, integration, security, automation, navigation, and educational resources.
Monte Carlo struggles with AI accuracy, user experience, anomaly detection, UI, monitor deletion, database features, and pricing competitiveness.
It would be great to see stronger AI-driven anomaly detection and predictive analytics to help identify potential issues before they impact performance.
Operations Manager at a financial services firm with 1,001-5,000 employees
We want to be able to customize the cost part, and we would appreciate more granular access control.
Service Manager at PwC
Having more transparent and granular cost control features would make it easier to manage usage.
Network Security Consultant at NTT DATA
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
 

Setup Cost

Datadog offers scalable, usage-based pricing but requires careful monitoring to manage escalating costs and optimize feature utilization.
Monte Carlo offers reasonable pricing for enterprise observability, with manageable setup costs and adaptable licensing for different organization sizes.
The setup cost for Datadog is more than $100.
Senior Performance and Architecture Analyst at a manufacturing company with 10,001+ employees
Pricing is mainly based on data ingestion, such as logs, metrics, and traces, and it can increase quickly if everything is enabled by default.
Cyber Security Consultant at HR Software Solution
Everybody wants the agent installed, but we only have so many dollars to spread across, so it's been difficult for me to prioritize who will benefit from Datadog at this time.
Applications Web Services Technical Engineer at Ace Hardware
I find it highly affordable for any organization sizes.
Project Superintendent at Teshama Group
 

Valuable Features

Datadog enhances operational efficiency with unified visibility, integration, customizable dashboards, and comprehensive monitoring across cloud platforms.
Monte Carlo enhances data reliability through AI-driven alerts, anomaly detection, and integration, reducing manual effort and improving decision-making.
Our architecture is written in several languages, and one area where Datadog particularly shines is in providing first-class support for a multitude of programming languages.
Senior Software Engineer at Los Angeles Times Communications, LLC
Having all that associated analytics helps me in troubleshooting by not having to bounce around to other tools, which saves me a lot of time.
Senior Site Reliability Engineer at a wholesaler/distributor with 5,001-10,000 employees
Datadog was able to find the alerts and trigger to notify our team in a very prompt manner before it got worse, allowing us to promptly adjust and remediate the situation in time.
Security Engineer at Invitation Homes
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
 

Categories and Ranking

Datadog
Average Rating
8.6
Reviews Sentiment
6.9
Number of Reviews
211
Ranking in other categories
Application Performance Monitoring (APM) and Observability (1st), Network Monitoring Software (4th), IT Infrastructure Monitoring (2nd), Log Management (4th), Container Monitoring (3rd), Cloud Monitoring Software (1st), AIOps (1st), Cloud Security Posture Management (CSPM) (5th), AI Observability (1st)
Monte Carlo
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
8
Ranking in other categories
Data Quality (23rd), Data Observability (1st)
 

Mindshare comparison

Datadog and Monte Carlo aren’t in the same category and serve different purposes. Datadog is designed for Cloud Monitoring Software and holds a mindshare of 5.8%, down 9.6% compared to last year.
Monte Carlo, on the other hand, focuses on Data Observability, holds 24.4% mindshare, down 32.2% since last year.
Cloud Monitoring Software Mindshare Distribution
ProductMindshare (%)
Datadog5.8%
Zabbix7.0%
SolarWinds NPM4.4%
Other82.8%
Cloud Monitoring Software
Data Observability Mindshare Distribution
ProductMindshare (%)
Monte Carlo24.4%
Unravel Data13.8%
Acceldata11.1%
Other50.699999999999996%
Data Observability
 

Featured Reviews

Dhroov Patel - PeerSpot reviewer
Site Reliability Engineer at Grainger
Has improved incident response with better root cause visibility and supports flexible on-call scheduling
Datadog needs to introduce more hard limits to cost. If we see a huge log spike, administrators should have more control over what happens to save costs. If a service starts logging extensively, I want the ability to automatically direct that log into the cheapest log bucket. This should be the case with many offerings. If we're seeing too much APM, we need to be aware of it and able to stop it rather than having administrators reach out to specific teams. Datadog has become significantly slower over the last year. They could improve performance at the risk of slowing down feature work. More resources need to go into Fleet Automation because we face many problems with things such as the Ansible role to install Datadog in non-containerized hosts. We mainly want to see performance improvements, less time spent looking at costs, the ability to trust that costs will stay reasonable, and an easier way to manage our agents. It is such a powerful tool with much potential on the horizon, but cost control, performance, and agent management need improvement. The main issues are with the administrative side rather than the actual application.
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.
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Top Industries

By visitors reading reviews
Financial Services Firm
15%
Manufacturing Company
9%
Computer Software Company
9%
Outsourcing Company
6%
Financial Services Firm
10%
Computer Software Company
8%
Construction Company
7%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business82
Midsize Enterprise49
Large Enterprise100
By reviewers
Company SizeCount
Small Business1
Midsize Enterprise3
Large Enterprise9
 

Questions from the Community

Any advice about APM solutions?
There are many factors and we know little about your requirements (size of org, technology stack, management systems, the scope of implementation). Our goal was to consolidate APM and infra monitor...
Datadog vs ELK: which one is good in terms of performance, cost and efficiency?
With Datadog, we have near-live visibility across our entire platform. We have seen APM metrics impacted several times lately using the dashboards we have created with Datadog; they are very good c...
Which would you choose - Datadog or Dynatrace?
Our organization ran comparison tests to determine whether the Datadog or Dynatrace network monitoring software was the better fit for us. We decided to go with Dynatrace. Dynatrace offers network ...
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...
 

Comparisons

 

Overview

 

Sample Customers

Adobe, Samsung, facebook, HP Cloud Services, Electronic Arts, salesforce, Stanford University, CiTRIX, Chef, zendesk, Hearst Magazines, Spotify, mercardo libre, Slashdot, Ziff Davis, PBS, MLS, The Motley Fool, Politico, Barneby's
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Find out what your peers are saying about Datadog, Zabbix, New Relic and others in Cloud Monitoring Software. Updated: May 2026.
900,747 professionals have used our research since 2012.