

Datadog and Gigamon Deep Observability Pipeline compete in the observability and monitoring sector. Datadog appears to have the upper hand due to its rich feature set and highly flexible monitoring environment.
Features: Datadog offers hosted functionalities, allowing users to not worry about infrastructure, and provides sharable dashboards and tags that are intuitive to use. Many integrations with platforms like AWS and Docker enhance its usability. Gigamon Deep Observability Pipeline excels in reducing data noise and delivering comprehensive insights into data flows to improve operational efficiency and security.
Room for Improvement: Datadog struggles with pricing complexity, documentation clarity, and advance notification systems. Users also call for streamlined integrations. Gigamon could enhance its platform with more intuitive interfaces, better cloud monitoring capabilities, and improved performance.
Ease of Deployment and Customer Service: Datadog is notably easy to deploy across public, private, and hybrid cloud environments with seamless integration. Its customer service is knowledgeable but occasionally slow in response times. Gigamon operates well in on-premises and hybrid clouds and provides excellent customer service with varying response speed and consistency.
Pricing and ROI: While Datadog's pricing is seen as high, its extensive feature set justifies the cost, offering positive ROI by enhancing developer productivity and system uptime. Meanwhile, Gigamon users appreciate its value for improving visibility and reducing the complexity of implementations, despite lacking detailed feedback on pricing dynamics.
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.
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.
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.
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.
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.
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 | Market Share (%) |
|---|---|
| Datadog | 6.0% |
| Gigamon Deep Observability Pipeline | 0.5% |
| Other | 93.5% |

| Company Size | Count |
|---|---|
| Small Business | 80 |
| Midsize Enterprise | 46 |
| Large Enterprise | 98 |
| 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 is a comprehensive network visibility solution that provides real-time insights into network traffic. It offers SSL inspection and mobile network monitoring for traffic monitoring purposes. The solution optimizes networks, aids in security inspection, and improves firewall performance. It is praised for its performance, power, straightforward integration, stability, and ease of initial setup.
With Gigamon, organizations can gain complete visibility into their network traffic, identify potential threats, and take proactive measures to prevent them. The solution is ideal for organizations of all sizes, including enterprises, service providers, and government agencies.
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