Watap Labs Unveils GPU Monitoring to Boost AI Infrastructure Efficiency

Watap Labs Unveils 'GPU Monitoring,' Key to the AI Era, Opening the Door to 'AI-Native Observability' Watap Labs, a leading observability company in Korea, has officially ushered in the era of 'AI-Native Observability.' As its first step, it introduces the 'Watap GPU Monitoring' solution, strengthening AI infrastructure observability capabilities in hybrid environments. AI-Native Observability, as defined by Watap Labs, signifies an innovative observability framework designed and implemented with AI at its core across the entire process, from data collection, interpretation, and automation to user experience. This goes beyond merely adding AI functionalities; it embodies a fundamental change that positions AI as a core axis from the technological philosophy and design stages. The newly unveiled 'Watap GPU Monitoring' focuses on real-time visualization and management of GPU resources, the core driving force of AI infrastructure. Lee Dong-in, CEO of Watap Labs, pointed out the current challenges, stating, "GPUs are no longer simple computing resources but have become a core competitive advantage for enterprises," and "Many companies are suffering losses due to their inability to properly ascertain GPU status." Watap Labs emphasized that through close collaboration with client companies, it has successfully resolved actual operational issues and established an environment where GPUs within the entire infrastructure can be integrated and observed. This solution supports optimal operations without resource waste by integrally monitoring key metrics such as GPU utilization, memory, temperature, and power consumption. Specifically, it reliably provides real-time monitoring, alert notifications, and long-term analysis functions not only in complex Kubernetes-based environments but also in hybrid environments spanning SaaS and on-premise. Choi Jin-sik, Head of Development, stated, "In a distributed AI infrastructure, the impact of failures is significant, and efficient resource utilization is essential given the short lifespan of GPUs," adding that the development focused on supporting the waste-free operation of high-cost resources. 'Watap GPU Monitoring' goes beyond simple utilization figures, visualizing the comprehensive connection relationships from GPUs to Pods and applications, thereby providing holistic insights. Notably, distinguishing itself from existing solutions with inadequate MIG (Multi-instance GPU) and Kubernetes integration, it innovatively improves resource tracking and problem diagnosis by clearly showing the organic connections between MIGs, Pods, and Nodes. This results in not only real-time monitoring and fault notifications but also root cause diagnosis, resource optimization, and even maximized collaboration efficiency between IT operations and development teams. CEO Lee Dong-in emphasized, "At a time when AI workloads and operational demands are exploding, a redefinition of operating platforms fitting the AI era is desperately needed." Starting with this GPU monitoring, Watap Labs plans to continuously introduce various product lines embedded with AI technology, setting new standards for IT operating environments and actively supporting enterprise business growth. As part of its AI-Native strategy, Watap Labs is also accelerating the development of AIOps and open-source monitoring tool support services. It currently services over 12,000 client projects in a public SaaS format and plans to continue expanding partnerships to strengthen AI competitiveness and operational efficiency for domestic and international companies.