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Grafana 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
4.8
Grafana improves data visualization, enhances operations, reduces AWS costs by 15%, and improves mean time to detect by 25%.
Sentiment score
6.9
Monte Carlo accelerates data issue detection by 60%-70% and reduces downtime by 40%-50%, saving 1,200 hours annually.
I identified over-provisioned servers and reduced my AWS monthly bill by 15%, which is a significant saving in terms of costs.
System engineer at a retailer with 10,001+ employees
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
7.1
Grafana's efficient customer service and strong open-source community provide valuable support and resources for technical 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.
The technical support team is very helpful with complex PromQL troubleshooting.
System engineer at a retailer with 10,001+ employees
My advice for people who are new to Grafana or considering it is to reach out to the community mainly, as that's the primary benefit of Grafana.
Sr. DevOps at a tech vendor with 1,001-5,000 employees
I do not use Grafana's support for technical issues because I have found solutions on Stack Overflow and ChatGPT helps me as well.
DevOps Team Lead at Kadabra
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
6.0
Grafana provides scalable solutions for visualization, though complexity and costs vary with deployment size and infrastructure needs.
Sentiment score
7.4
Monte Carlo scales effectively, accommodating increased data demands and providing flexibility for organizations experiencing growth and expanding data volumes.
It is highly scalable and built on a big data architecture capable of ingesting trillions of data points.
System engineer at a retailer with 10,001+ employees
In terms of our company, the infrastructure is using two availability zones in AWS.
DevOps Team Lead at Kadabra
In assessing Grafana's scalability, we started noticing logs missing or metrics not syncing in time.
Sr. DevOps at a tech vendor with 1,001-5,000 employees
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
7.9
Grafana is stable and reliable with minor issues; performance varies based on resource configuration and architectural factors.
Sentiment score
8.7
Users praise Monte Carlo for its stable and reliable performance, noting its consistent uptime and absence of crashes.
When something in their dashboard does not work, because it is open source, I am able to find all the relative combinations that people are having, making it much easier for me to fix.
DevOps Team Lead at Kadabra
Once you get to a higher load, you need to re-evaluate your architecture and put that into account.
Sr. DevOps at a tech vendor with 1,001-5,000 employees
Even when handling millions of data points, the visualization layer remains responsive.
System engineer at a retailer with 10,001+ employees
I did not see any issues with respect to stability.
Principal Data Engineer at Teradata Corporation
 

Room For Improvement

Grafana users seek enhanced dashboard usability, AI features, integration ease, user interface, flexible licensing, and better security and compatibility.
Monte Carlo struggles with AI accuracy, user experience, anomaly detection, UI, monitor deletion, database features, and pricing competitiveness.
It would be better if they made the technology easy to use without needing to read extensive documentation.
AWS Cloud Re-Start Program Specialist at Orange RDC (Congo)
Grafana cannot be easily embedded into certain applications and offers limited customization options for graphs.
BI and Analytics Engineer at Sandvine Inc
I would want to see improvements, especially in the tracing part, where following different requests between different services could be more powerful.
Director of Engineering at a insurance company with 10,001+ employees
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

Grafana provides flexible pricing, from a free version to paid tiers, appealing to varied enterprise needs and scalable deployments.
Monte Carlo offers reasonable pricing for enterprise observability, with manageable setup costs and adaptable licensing for different organization sizes.
In an enterprise setting, pricing is reasonable, as many customers use it.
Aplication Architect at Amazon
The costs associated with using Grafana are somewhere in the ten thousands because we are able to control the logs in a more efficient way to reduce it.
DevOps Team Lead at Kadabra
I purchased my Grafana Cloud subscription through the AWS Marketplace, which simplified my procurement process and allowed me to apply the cost towards my AWS committed spend.
System engineer at a retailer with 10,001+ employees
I find it highly affordable for any organization sizes.
Project Superintendent at Teshama Group
 

Valuable Features

Grafana is praised for its customizable dashboards, integration, real-time monitoring, open-source nature, and broad community support.
Monte Carlo enhances data reliability through AI-driven alerts, anomaly detection, and integration, reducing manual effort and improving decision-making.
Users can monitor metrics with greater ease, and the tool aids in quickly identifying issues by providing a visual representation of data.
Aplication Architect at Amazon
The fact that I can join data from my SQL database with metrics from Prometheus in the same table is a feature I have not found performed as well elsewhere.
System engineer at a retailer with 10,001+ employees
You can check those metrics in the incident management tool by filtering the alert source as Grafana, and it helps in reducing production incidents because you can acknowledge and visualize the metrics from Grafana on time.
Senior Site Reliability Engineer at a tech vendor with 501-1,000 employees
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

Grafana
Average Rating
8.0
Reviews Sentiment
6.9
Number of Reviews
49
Ranking in other categories
Application Performance Monitoring (APM) and Observability (7th)
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

Grafana and Monte Carlo aren’t in the same category and serve different purposes. Grafana is designed for Application Performance Monitoring (APM) and Observability and holds a mindshare of 2.6%, down 6.7% compared to last year.
Monte Carlo, on the other hand, focuses on Data Observability, holds 24.4% mindshare, down 32.2% since last year.
Application Performance Monitoring (APM) and Observability Mindshare Distribution
ProductMindshare (%)
Grafana2.6%
Dynatrace5.3%
Datadog4.6%
Other87.5%
Application Performance Monitoring (APM) and Observability
Data Observability Mindshare Distribution
ProductMindshare (%)
Monte Carlo24.4%
Unravel Data13.8%
Acceldata11.1%
Other50.699999999999996%
Data Observability
 

Featured Reviews

BasilJiji - PeerSpot reviewer
System engineer at a retailer with 10,001+ employees
Unified dashboards have empowered teams and have democratized real-time operational insights
Grafana's snapshot and dashboard sharing features are critical for our remote incident response. During production issues, I generate a public snapshot of a dashboard at a specific point and share the URL in our Slack war room so every engineer can see exactly what the metrics looked like when the error occurred. This helps significantly during the process of finding the root cause in those scenarios. The best features Grafana offers go beyond just pretty charts; it is an integration engine. The fact that I can join data from my SQL database with metrics from Prometheus in the same table is a feature I have not found performed as well elsewhere. My team uses this feature by comparing two different tables from the databases to show one single view, which Grafana is really helping with. In a visualized way, the charts can be displayed on one dashboard, allowing end users who are not familiar with these technical aspects to extract valuable data from it. Grafana has positively impacted our organization by democratizing data within our company. Before using Grafana, only developers could see the system health, but now our product managers and executives have their own high-level dashboards, which has improved cross-departmental transparency and alignment.
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
18%
Computer Software Company
10%
Manufacturing Company
9%
Comms Service Provider
7%
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 Business13
Midsize Enterprise10
Large Enterprise27
By reviewers
Company SizeCount
Small Business1
Midsize Enterprise3
Large Enterprise9
 

Questions from the Community

What is your experience regarding pricing and costs for Grafana?
My experience with pricing, setup cost, and licensing is that it is very reasonable and has excellent community support.
What needs improvement with Grafana?
Currently, I do not think that any improvement is required, but there are multiple use cases.
What is your primary use case for Grafana?
My main use case for Grafana is to create and design dashboards based on the metrics provided by different exporters via Prometheus. We have different exporters, and we are creating different dashb...
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

Microsoft, Adobe, Optum, Sky, Nvidia, Roblox, Wells Fargo, BlackRock, Informatica, Maersk, Daimler Truck, SNCF, Atlassian, DHL, SAP, JPMorgan Chase, Cisco, Citi and many others.
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