

Find out what your peers are saying about Monte Carlo, Informatica, Unravel Data and others in Data Observability.
It definitely reduces resource hours needed for work, lessening the effort required significantly compared to when Monte Carlo is not in place.
Monte Carlo has solved the challenge of monitoring ingestion health at scale.
Monte Carlo saves me roughly 30% to 40% of my time in doing verifications or data quality checks.
We have saved considerable amounts of money, reducing our expenditures from around three to four crores to approximately one to one point two crores.
We have been able to save a great deal of money, and our profits have increased by twenty percent.
Using Splunk has saved my organization about 30% of our budget compared to using multiple different monitoring products.
When I requested help regarding the deletion of monitors, I received a very good and quick response.
Monte Carlo's customer support team responds very fast.
My experiences reaching out to them show that they were very quick to help and very professional.
On a scale of 1 to 10, the customer service and technical support deserve a 10.
They have consistently helped us resolve any issues we've encountered.
The customer support system is the foundational pillar of any successful business.
Monte Carlo's scalability is impressive.
As our company's business grows and the data volume increases, Monte Carlo scales very well.
Monte Carlo is robust and scalable for our data needs.
We've used the solution across more than 250 people, including engineers.
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.
We have never seen any kind of downtime or crashes, as it has been absolutely very easy to scale.
I did not see any issues with respect to stability.
When downtime occurs, it raises concerns about how we measure and receive alerts, as everything needs to be in place.
Splunk Observability Cloud is very stable.
It is highly scalable because it can handle approximately up to one hundred applications at a time without any lapse or lag.
Artificial intelligence can access multiple systems underneath Monte Carlo, such as any kind of database or any kind of real-time source systems.
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.
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.
The out-of-the-box customizable dashboards in Splunk Observability Cloud are very effective in showcasing IT performance to business leaders.
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.
There should be a solution to update OTeL agents from Splunk Observability Cloud itself.
I find it highly affordable for any organization sizes.
Splunk is a bit expensive since it charges based on the indexing rate of data.
It is expensive, especially when there are other vendors that offer something similar for much cheaper.
I can confidently say our availability improved by forty percent, and downtime was reduced by approximately seventy to eighty percent.
Monte Carlo has accelerated the development process and has reduced the testing time significantly.
The system does not send false alerts.
Monte Carlo has positively impacted my organization by significantly reducing manual tasks.
Splunk provides advanced notifications of roadblocks in the application, which helps us to improve and avoid impacts during high-volume days.
For troubleshooting, we can detect problems in seconds, which is particularly helpful for digital teams.
It offers unified visibility for logs, metrics, and traces.
| Product | Mindshare (%) |
|---|---|
| Monte Carlo | 24.4% |
| Unravel Data | 13.8% |
| Acceldata | 11.1% |
| Other | 50.699999999999996% |
| Product | Mindshare (%) |
|---|---|
| Splunk Observability Cloud | 2.3% |
| Dynatrace | 5.3% |
| Datadog | 4.6% |
| Other | 87.8% |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 3 |
| Large Enterprise | 9 |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 8 |
| Large Enterprise | 55 |
Monte Carlo offers a comprehensive data observability platform that ensures reliable data pipelines and prevents data downtime by providing real-time monitoring and alerting, making it a crucial tool for data-driven organizations.
Monte Carlo provides end-to-end visibility into data infrastructure, helping teams quickly identify, troubleshoot, and resolve data issues. This prevents costly data incidents and improves data trust. As data systems become more complex, maintaining accurate and timely data is challenging; Monte Carlo addresses this by integrating with popular data stack tools, allowing users to gain insights and maintain data reliability without missing critical data anomalies.
What are the key features of Monte Carlo?In finance, Monte Carlo enhances data accuracy for compliance and reporting. Retail businesses use it to optimize inventory and customer insights, while healthcare benefits from improved data handling for patient management. By ensuring robust data infrastructure, Monte Carlo supports diverse industry needs.
Splunk Observability Cloud offers sophisticated log searching, data integration, and customizable dashboards. With rapid deployment and ease of use, this cloud service enhances monitoring capabilities across IT infrastructures for comprehensive end-to-end visibility.
Focused on enhancing performance management and security, Splunk Observability Cloud supports environments through its data visualization and analysis tools. Users appreciate its robust application performance monitoring and troubleshooting insights. However, improvements in integrations, interface customization, scalability, and automation are needed. Users find value in its capabilities for infrastructure and network monitoring, as well as log analytics, albeit cost considerations and better documentation are desired. Enhancements in real-time monitoring and network protection are also noted as areas for development.
What are the key features?In industries, Splunk Observability Cloud is implemented for security management by analyzing logs from detection systems, offering real-time alerts and troubleshooting for cloud-native applications. It is leveraged for machine data analysis, improving infrastructure visibility and supporting network and application performance management efforts.
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