The rise of AI-ready private clouds

"The conversation around enterprise AI infrastructure has shifted dramatically in the past 18 months. While public cloud providers continue to dominate headlines with their latest GPU offerings and managed AI services, a quiet revolution is taking place in enterprise data centers: the rapid rise of Kubernetes-based private clouds as the foundation for secure, scalable AI deployments. This isn't about taking sides between public and private clouds-the decision was made years ago."

"First, there's the economics. While public cloud excels at handling variable workloads and providing instant scalability, the costs can spiral quickly for sustained, high-compute workloads-exactly the profile of most AI applications. Running large language models in the public cloud can be extremely expensive. For instance, AWS instances with H100 GPUs cost about $98,000 per month at full utilization, not including data transfer and storage costs."

"According to Gartner, 90% of organizations will adopt hybrid cloud approaches by 2027. The reasons are both practical and profound. Second, data gravity remains a powerful force. The cost and complexity of moving this data to the public cloud make it far more practical to bring compute to the data rather than the reverse. Why? The global datasphere will reach 175 zettabytes by 2025, with 75% of enterprise-generated data created and processed outside traditional cen"

Enterprise data centers are rapidly adopting Kubernetes-based private clouds to support secure, scalable AI deployments. AI workloads' high compute demands, data sovereignty, compliance requirements, and cost concerns are driving enterprises toward private and hybrid cloud architectures. Public cloud excels at elasticity for variable workloads but becomes expensive for sustained, high-compute AI tasks; AWS H100 instances can cost roughly $98,000 per month at full utilization, excluding data transfer and storage. Data gravity and the growing global datasphere make moving large datasets impractical, favoring bringing compute to data and reinforcing hybrid strategies. Enterprises are building AI-ready private clouds to match public cloud capabilities while preserving control and cost efficiency.

Read at InfoWorld