

Datadog and Prometheus are both prominent players in the IT monitoring industry. Datadog has an advantage with its vast integration options and user-friendly features.
Features: Datadog offers a hosted solution that doesn't require infrastructure management, integrates seamlessly with various tools, and provides a comprehensive view through dashboards, alerts, and anomaly detection. Prometheus excels in reliable metric collection and is praised for its open-source flexibility but lacks the breadth of ready-to-use features compared to Datadog.
Room for Improvement: Datadog could improve data representation, user insight, and pricing simplicity. Users also request more pre-configured alerts. Prometheus needs a more intuitive user interface, better query capabilities, and could benefit from integrated visualization and logging features.
Ease of Deployment and Customer Service: Datadog is valued for its adaptability across cloud environments and solid technical support, although some note response inconsistencies. Prometheus is favored for on-premises setups, but users often rely on community resources for support due to its technical complexity.
Pricing and ROI: Datadog, with its modular and usage-based pricing, is seen as costly for large implementations but offers high ROI in operational efficiency. Unexpected costs, however, are a concern. Prometheus, being open-source, offers cost benefits with no licensing fees, appealing to companies aiming to minimize monitoring expenses.
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.
I believe features that would provide a lot of time savings, just enabling you to really narrow down and filter the type of frustration or user interaction that you're looking for.
Using open-source Prometheus saves me money compared to AWS native services.
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.
Prometheus does not offer traditional technical support.
Datadog's scalability has been great as it has been able to grow with our needs.
We did, as a trial, engage the AWS integration, and immediately it found all of our AWS resources and presented them to us.
Datadog's scalability is strong; we've continued to significantly grow our software, and there are processes in place to ensure that as new servers, realms, and environments are introduced, we're able to include them all in Datadog without noticing any performance issues.
Prometheus is scalable, with a rating of ten out of ten.
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.
Datadog seems to be more stable, and I really want to have a complete demo before making a call to decide on this.
Deploying it on multiple instances or using Kubernetes for automatic management has enhanced its 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.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
The setup cost for Datadog is more than $100.
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.
My experience with pricing, setup cost, and licensing is that it is really expensive.
Prometheus is cost-effective for me as it is free.
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.
It allows me to save money by avoiding costs associated with AWS native services like CloudWatch or Amazon Prometheus.
| Product | Market Share (%) |
|---|---|
| Datadog | 6.5% |
| Zabbix | 9.6% |
| PRTG Network Monitor | 5.0% |
| Other | 78.9% |
| Product | Market Share (%) |
|---|---|
| Prometheus-AI Platform | 1.7% |
| IBM Maximo | 15.4% |
| Oracle Enterprise Asset Management | 8.5% |
| Other | 74.4% |


| Company Size | Count |
|---|---|
| Small Business | 80 |
| Midsize Enterprise | 46 |
| Large Enterprise | 99 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 8 |
| Large Enterprise | 13 |
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.
Prometheus-AI Platform offers flexible solutions for collecting, visualizing, and comparing metrics, appreciated for its scalability, rich integrations, and open-source adaptability.
Prometheus-AI Platform provides a reliable framework for monitoring and analyzing metrics across diverse environments. With extensive API support, it supports data collection, querying, and visualization, integrating seamlessly with tools like Grafana. High availability, scalability, and lightweight configuration make it suitable for traditional and microservice environments, while community support enhances its utility. Though its query language and interface require improvements for better ease of use, and with calls for stronger integration options, the platform remains a leading choice for comprehensive metric analysis.
What are Prometheus-AI Platform's main features?Companies leverage Prometheus-AI Platform across various industries, utilizing it to monitor and analyze metrics from applications and infrastructure. It is extensively used in financial services and IT sectors for collecting, scraping logs, and monitoring Kubernetes deployments. Deployed both on-premise and in cloud environments like Azure and Amazon, it supports system and application metrics analysis, ensuring a comprehensive view for developers.
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