US EditionChronosphere Named a Leader for Second Consecutive Year in the 2025 Gartner® Magic Quadrant™ for Observability Platforms
AUSTRALIA, JUL 10 – Datadog's use of extended Berkeley Packet Filter (eBPF) technology cuts CPU usage by 35% and improves network connection attribution for containers and services.
- On July 10, 2025, Datadog, a cloud application monitoring and security provider, was recognized as a Leader for the fifth year in a row in Gartner’s 2025 evaluation of observability solutions.
- This recognition came after Datadog enhanced its real-time monitoring capabilities by deploying advanced kernel probes and process monitoring techniques based on eBPF technology, which helped overcome the shortcomings of earlier legacy approaches.
- These enhancements reduced CPU usage by 35%, improved accuracy in attributing network connections, eliminated throttling, and increased visibility into short-lived processes, while features like APM and Watchdog helped quickly remediate issues.
- Yanbing Li, Datadog’s Chief Product Officer, highlighted the importance of being recognized by Gartner among the top vendors in their annual evaluation of observability platforms for the fifth consecutive year, underscoring the company’s commitment to customer-driven innovation and the integration of AI technologies.
- Datadog plans to further expand eBPF usage to enable deeper data correlation and advanced observability features, aiming to maintain market leadership and enhance product strategy through ongoing user collaboration.
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Datadog Boosts Network Observability with eBPF, Cuts CPU Usage by 35%
Datadog has significantly improved the performance and accuracy of its Cloud Network Monitoring (CNM) by leveraging eBPF, eliminating previous limitations caused by legacy Netlink and API polling methods. The move resulted in a 35% reduction in CPU usage and more accurate attribution of network connections to containers and services. Earlier, CNM relied on Netlink and container runtime APIs to track NAT and container data, which led to data loss…
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