

Find out what your peers are saying about Datadog, Splunk, New Relic and others in Cloud Monitoring Software.
Previously we had thirteen contractors doing the monitoring for us, which is now reduced to only five.
Datadog has delivered more than its value through reduced downtime, faster recovery, and infrastructure optimization.
We have also seen fewer escalations for minor issues because alerts help us catch problems earlier, which indirectly reduces downtime and improves overall efficiency.
It definitely reduces resource hours needed for work, lessening the effort required significantly compared to when Monte Carlo is not in place.
Monte Carlo saves me roughly 30% to 40% of my time in doing verifications or data quality checks.
We have saved more than three-fourths of the time in the testing phase.
When I have additional questions, the ticket is updated with actual recommendations or suggestions pointing me in the correct direction.
Overall, the entire Datadog comprehensive experience of support, onboarding, getting everything in there, and having a good line of feedback has been exceptional.
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.
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.
Technical support is satisfactory from them. Even though the product application team is not that much larger, they are still giving better support.
Datadog's scalability has been great as it has been able to grow with our needs.
Since it is a SaaS platform, we did not have to worry about backend scaling.
We have not faced any major performance issues from the platform side; it handles increased metrics and monitoring loads smoothly.
Monte Carlo demonstrates scalability in adopting new models automatically, which should serve organizations well.
Monte Carlo's scalability is impressive.
As our company's business grows and the data volume increases, Monte Carlo scales very well.
Metrics collection and alerting have been consistent in day-to-day use.
Datadog is very stable, as there hasn't been any downtime or issues since I've been here, and it's always on time.
Datadog seems stable in my experience without any downtime or reliability issues.
The accuracy is 100% from what I have noticed.
I did not see any issues with respect to stability.
Monte Carlo is stable, with ongoing feature improvements.
It would be great to see stronger AI-driven anomaly detection and predictive analytics to help identify potential issues before they impact performance.
We want to be able to customize the cost part, and we would appreciate more granular access control.
Having more transparent and granular cost control features would make it easier to manage usage.
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 setup cost for Datadog is more than $100.
Pricing is mainly based on data ingestion, such as logs, metrics, and traces, and it can increase quickly if everything is enabled by default.
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.
In terms of pricing, setup cost, and licensing, I rate it a bit high on the pricing side; it is pricey, but given the features and flexibility it offers during implementation, it stands out against specific libraries that are less handy to use.
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.
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.
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.
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.
| Product | Mindshare (%) |
|---|---|
| Datadog | 5.3% |
| Zabbix | 6.6% |
| SolarWinds NPM | 4.6% |
| Other | 83.5% |
| Product | Mindshare (%) |
|---|---|
| Monte Carlo | 25.9% |
| Unravel Data | 11.1% |
| Informatica Intelligent Data Management Cloud (IDMC) | 10.7% |
| Other | 52.3% |


| Company Size | Count |
|---|---|
| Small Business | 81 |
| Midsize Enterprise | 50 |
| Large Enterprise | 100 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 3 |
| Large Enterprise | 14 |
Datadog offers a comprehensive platform for monitoring applications and infrastructure, providing insights into real-time metrics and optimizing performance with extensive integrations and analytics capabilities.
Datadog provides a robust monitoring platform designed for applications and infrastructure tracking. With intuitive dashboards and real-time metrics visibility, it enhances operational efficiency and enables proactive incident management. The platform supports data centralization across services, reducing downtime through its monitoring capabilities. Integration with AWS, Kubernetes, and other systems aligns with performance tracking and infrastructure management, while APM and RUM features allow tracing and user behavior analytics.
What are Datadog's Key Features?Industries utilizing Datadog benefit from detailed monitoring and comprehensive analytics across multi-cloud environments. Companies in e-commerce, finance, and technology sectors implement it for end-to-end monitoring ensuring robust application performance. The platform assists in maintaining standards through integration with services like AWS and Azure, providing efficient observability and allowing proactive issue resolution.
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.
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