Enterprises need economically scalable capacity to keep pace with the data created by AI inference
Ask most executives to picture the cost of AI and they'll likely see GPUs—racks of accelerators with gigawatts of power—and a waitlist for silicon. But that picture leaves something out: Data storage costs
What happens to data after a model responds? Much of the back-end data that's generated—from billions of daily prompts, images, and videos—needs to be stored. But data storage is one of the harder infrastructure costs to plan for, and it's often overlooked in initial AI infrastructure planning
