AI’s hidden storage bill: What inference leaves behind
This piece examines the often-overlooked infrastructure costs associated with AI inference, specifically data storage. It argues that while training models is a finite cost, the continuous nature of inference creates a massive, growing data footprint.
Why it matters
As enterprises scale generative AI, the hidden costs of data storage could significantly impact the economic viability of AI projects.
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.
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