Okestro and NetApp have signed a memorandum of understanding to develop integrated infrastructure for stable enterprise AI operations. As announced on January 22nd, the partnership combines Okestro's full-stack AI infrastructure software with NetApp's data orchestration and enterprise-grade disaster recovery technology. The core objective is to bundle AI inference environments, data management, and disaster recovery systems into a single architecture, addressing the growing challenge of balancing performance and cost efficiency in enterprise AI adoption. According to Yoo Jae-sung, CEO of NetApp Korea, companies want to expand AI adoption but face the burden of balancing performance and cost efficiency, making this collaboration essential for supporting enterprise AI scaling.
The partnership combines Okestro's comprehensive AI infrastructure platform with NetApp's data management expertise. Okestro supplies the full-stack software including 'CONTRABASS' server virtualization solution, 'VIOLA' cloud-native operations management platform, and 'CONCERTO AI' AI inference operations platform. NetApp provides the necessary data management capabilities for AI data pipeline operations through API-based connectivity integration and dedicated engineering support. The companies plan to pursue integrated validation of GPU-accelerated AI inference, large-scale data pipelines, and high-performance storage in a single environment, with the goal of dramatically reducing AI service interruption risks and enhancing data stability.
The AI inference infrastructure market is experiencing explosive growth, with industry analysts projecting expansion from US$5 billion in 2024 to US$48.8 billion by 2030. According to reports from Business Standard, this growth reflects how quickly organisations are transitioning from experimentation to production-level AI deployment. However, enterprises are now paying closer attention to the cost of every AI interaction, including the infrastructure resources required to process each token generated by large language models. The real cost of AI begins after deployment, as every prompt submitted by employees and customer query answered by virtual assistants depends on continuous inferencing processes that consume infrastructure resources with every interaction.
The partnership addresses critical disaster recovery challenges as AI adoption expands beyond pilot phases into actual services. The companies plan to jointly establish a reference architecture and operational model for Active-Active structure, where two data centers operate simultaneously so that if one fails, the other can immediately continue service. This approach is expected to enable enterprise customers to dramatically reduce the risk of AI service interruptions and enhance data stability. The collaboration leverages NetApp's Technology Alliance Program to jointly develop the AI and data infrastructure ecosystem and establish a joint go-to-market strategy targeting both South Korean and international markets.
The rapid evolution of enterprise AI is creating entirely new engineering disciplines that extend far beyond traditional machine learning roles. As reported by multiple industry sources, LLM Engineers have become some of the most sought-after professionals, designing production applications powered by foundation models using prompt engineering, Retrieval-Augmented Generation (RAG), evaluation frameworks, and orchestration techniques. AI Platform Engineers create internal infrastructure enabling organizations to develop, deploy, monitor, and govern AI systems efficiently, building model registries, experimentation environments, and deployment pipelines. AI Infrastructure Engineers build sophisticated cloud environments optimized for GPU scheduling, distributed inference, networking, storage, model serving, caching, autoscaling, and workload management. These roles require expertise in orchestration frameworks, memory management, planning algorithms, reasoning workflows, tool integration, and secure execution environments.
