

Datadog and Gigamon Deep Observability Pipeline are key competitors in network monitoring and data integration. Datadog appears to have the upper hand due to its extensive feature set and ease of deployment.
Features: Datadog is known for its comprehensive toolset including sharable dashboards, extensive cloud integrations, and automated templates. Its dashboards allow for easier metric visualization and improved server monitoring. Gigamon focuses on packet filtering, traffic aggregation, and encryption, providing deep network visibility and data transformation capabilities.
Room for Improvement: Datadog needs better cost transparency and real-time data usage metrics. Enhanced integration with security tools is often requested by users. Gigamon should work on improving cloud monitoring, user interface usability, and cluster capacity. Both could benefit from stability and intuitive setup improvements.
Ease of Deployment and Customer Service: Datadog excels in public and hybrid cloud deployments, backed by strong customer service that offers quick and knowledgeable responses. Gigamon is tailored for on-premises deployments but still maintains a high standard of customer service. Datadog's real-time chat support, however, shows variability depending on the region. Gigamon users see room for improvement in support response times.
Pricing and ROI: Datadog's pricing is flexible but users cite hidden costs, suggesting a need for better cost management tools. It provides a strong ROI through operational efficiencies and debugging time savings. Gigamon, while often seen as expensive, justifies its cost with noise reduction and optimized data flows. Both offer different pricing complexities impacting ROI.
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.
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.
The technical support by Gigamon Deep Observability Pipeline is good because it has a local architect in my area.
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.
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.
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.
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.
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.
The Pipeline's Comprehensive Insights into data flows have helped improve operational efficiency and security.
| Product | Mindshare (%) |
|---|---|
| Datadog | 4.7% |
| Gigamon Deep Observability Pipeline | 0.6% |
| Other | 94.7% |

| Company Size | Count |
|---|---|
| Small Business | 82 |
| Midsize Enterprise | 47 |
| Large Enterprise | 100 |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 1 |
| Large Enterprise | 5 |
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.
Gigamon Deep Observability Pipeline boosts network visibility and performance through features like NetFlow and deduplication, facilitating data flow insights and improved security. It supports traffic monitoring and management across various infrastructures.
Gigamon Deep Observability Pipeline enhances network management by offering features such as NetFlow, deduplication, header stripping, and packet filtering. These capabilities are instrumental in optimizing performance, offering users stability and improved encryption processes. Despite its robust hardware capabilities, it requires enhancements in security, filtering, and delivery time for hardware. Users note challenges with monitoring cloud networks and insufficient cluster capacity. There is also a call for improved interface design and internal traffic flow visualization.
What are the essential features of Gigamon Deep Observability Pipeline?Gigamon Deep Observability Pipeline finds application across industries for network visibility and management. It is used extensively for traffic monitoring, SSL inspection, mobile network oversight, and data center operations. Organizations leverage its capabilities to address network issues, enhance security, and streamline performance monitoring processes. Its ability to group traffic aids significantly in problem-solving and SSL detection.
We monitor all Application Performance Monitoring (APM) and Observability reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.