This Cloud Native Now feature by Nathan Eddy examines what happens when enterprises run AI workloads without proper orchestration: GPU waste, job starvation, dependency conflicts, and runaway cloud bills — likened to running a data center without a traffic controller, where everything eventually collides. The article argues that most organizations adopted AI without thinking through the operational impacts, focusing on training and deploying models quickly rather than on managing infrastructure and orchestration efficiently. As CloudBolt COO Yasmin Rajabi explains, because optimizing a GPU is far harder than a CPU, teams end up with expensive, underutilized accelerators or workloads fighting over the same resources, driving both rising costs and performance degradation as they scale beyond a proof of concept. Kubernetes is presented as the antidote — a declarative, automated approach to allocating and scaling resources, with ecosystem tools for real-time GPU partitioning that keep utilization high as pods are scheduled. The piece then turns to Kamiwaza's James Urquhart, Field CTO and Technology Evangelist, on what proper AI orchestration delivers beyond raw scheduling. He describes distributing inference across data locations to remove the latency and inefficiency of moving data over the network or replicating it near compute; building a metadata graph and ontology across existing data sources so orchestration understands the data landscape and can route each request to where the relevant data resides; and understanding the infrastructure landscape to place workloads intelligently. It's a strong articulation of Kamiwaza's distributed-orchestration thesis. Read the full article on Cloud Native Now.
Source: Cloud Native Now — Read the full article
