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AI’s hidden storage bill: What inference leaves behind

Tim Rausch· 22 octobre 2026

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…

Résumé

AI costs beyond GPUs

When executives plan for AI expenses, they often focus on GPUs (Graphics Processing Units), the powerful chips that handle complex calculations for AI models.

However, this view overlooks a significant hidden cost: data storage.

While training AI models is a one-time, compute-intensive process, inference—the act of using a trained model to generate outputs like text, images, or videos—runs continuously.

Every inference request produces additional data, including logs, cached context, intermediate files, backups, and safety records.

Unlike training, which ends after a project, inference generates persistent data that accumulates over time.

For example, an 8-second AI-generated video (3.97MB for the user) can leave behind over 500MB of back-end data, a 130-fold increase.

This hidden storage burden grows as AI adoption scales, making it a critical but often underestimated part of AI infrastructure costs.

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