

Find out what your peers are saying about Datadog, Zabbix, 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 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.
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.
My experiences reaching out to them show that they were very quick to help and very professional.
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'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.
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.
I did not see any issues with respect to stability.
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.
I find it highly affordable for any organization sizes.
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 | 5.8% |
| Zabbix | 7.0% |
| SolarWinds NPM | 4.4% |
| Other | 82.8% |
| Product | Mindshare (%) |
|---|---|
| Monte Carlo | 24.4% |
| Unravel Data | 13.8% |
| Acceldata | 11.1% |
| Other | 50.699999999999996% |

| Company Size | Count |
|---|---|
| Small Business | 82 |
| Midsize Enterprise | 49 |
| Large Enterprise | 100 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 3 |
| Large Enterprise | 9 |
Datadog integrates extensive monitoring solutions with features like customizable dashboards and real-time alerting, supporting efficient system management. Its seamless integration capabilities with tools like AWS and Slack make it a critical part of cloud infrastructure monitoring.
Datadog offers centralized logging and monitoring, making troubleshooting fast and efficient. It facilitates performance tracking in cloud environments such as AWS and Azure, utilizing tools like EC2 and APM for service management. Custom metrics and alerts improve the ability to respond to issues swiftly, while real-time tools enhance system responsiveness. However, users express the need for improved query performance, a more intuitive UI, and increased integration capabilities. Concerns about the pricing model's complexity have led to calls for greater transparency and control, and additional advanced customization options are sought. Datadog's implementation requires attention to these aspects, with enhanced documentation and onboarding recommended to reduce the learning curve.
What are Datadog's Key Features?In industries like finance and technology, Datadog is implemented for its monitoring capabilities across cloud architectures. Its ability to aggregate logs and provide a unified view enhances reliability in environments demanding high performance. By leveraging real-time insights and integration with platforms like AWS and Azure, organizations in these sectors efficiently manage their cloud infrastructures, ensuring optimal performance and 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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