ArcSight Logger and Datadog are competitors in the log management and analytics category. Datadog appears to have the upper hand due to its real-time monitoring capabilities and intuitive dashboards.
Features: ArcSight Logger provides scalable data collection, SmartConnectors for integration, and comprehensive querying capabilities. It is robust for enterprise environments due to its ability to manage large volumes of data. Datadog offers real-time cloud monitoring, seamless integration options, and user-friendly dashboard creation. Its customization capabilities for dashboards and real user monitoring enhance the analytics experience.
Room for Improvement: ArcSight Logger needs enhanced analytics, a more intuitive interface, and better support for emerging technologies. Complexity and outdated features also require modern enhancements. Datadog faces challenges with pricing complexity and high costs with scaling use. Documentation inconsistencies and the need for a more user-friendly interface are other areas for improvement.
Ease of Deployment and Customer Service: ArcSight Logger predominantly offers on-premises deployment, requiring on-site maintenance that can be challenging without adequate technical capacity. Support ratings vary, with some dissatisfaction reported post-acquisitions. Datadog provides versatile deployment options from private to hybrid cloud setups, supporting varied environments. Known for responsive customer service, Datadog also offers extensive documentation to facilitate cloud-centric scalability and onboarding.
Pricing and ROI: ArcSight Logger is noted for higher costs, reflecting enterprise-grade offerings and complexity, with investment justified for security and compliance benefits. Its pricing is high compared to competitors but aligns with enterprise functionalities. Datadog's usage-based pricing is criticized for unpredictability and scaling costs. Despite this, users acknowledge the value gained and efficiency improved, with ROI dependent on usage discipline and cost management strategies.
We provide pre-implementation, implementation, and post-implementation support.
Splunk does much more than SIEM, including log analysis, user behavior analysis, threat intelligence, and customer behavior analysis.
The documentation is adequate, but team members coming into a project could benefit from more guided, interactive tutorials, ideally leveraging real-world data.
In future updates, I would like to see AI features included in Datadog for monitoring AI spend and usage to make the product more versatile and appealing for the customer.
There should be a clearer view of the expenses.
The setup cost for Datadog is more than $100.
ArcSight Logger installs on very minimal resources with very few requirements
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.
The technology itself is generally very useful.
| Product | Market Share (%) |
|---|---|
| Datadog | 5.7% |
| ArcSight Logger | 0.7% |
| Other | 93.6% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 10 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 78 |
| Midsize Enterprise | 42 |
| Large Enterprise | 82 |
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.
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