Inference adds a growing storage burden to AI budgets

AI inference generates a growing storage bill that compute-focused infrastructure budgets can overlook. Unlike finite training runs, inference operates continuously, leaving behind logs, cached context, synthetic outputs, backups and safety records. Those retained assets accumulate even when compute demand slows, creating ongoing capacity and retrieval requirements.

Internal WD measurements found that an 8-second video delivered as a 3.97MB 720p file left more than 500MB of associated back-end data, a 130x increase over the visible output. A WD-sponsored IDC survey of 763 IT and business decision makers found nearly 95% reported increased data volumes from generative AI over the past 12 months. Roughly 61% reported growth of 25% or more.

Retained information is also becoming an input for future workloads rather than remaining untouched in archives. The same survey found 96% of respondents anticipate needing faster archive retrieval for inference or retrieval-augmented generation. Infrastructure planning therefore needs to balance rapid access with economical long-term capacity across storage tiers. WD positions HDDs as a cost-effective foundation for large retained datasets. For finance and technology leaders, the budgeting challenge extends beyond procurement to retention policies, power, reliability and operating efficiency, with storage delays potentially leaving expensive GPUs idle.