AI's Hidden Storage Bill: What Inference Leaves Behind
Enterprises need economically scalable capacity to keep pace with the data created by AI inference.
TL;DR
- Data storage costs are a significant, often underestimated, expense in AI infrastructure planning, beyond the initial focus on GPUs and compute.
- AI inference, unlike finite model training, is continuous, with each interaction generating back-end data that needs storage and contributes to a growing data footprint.
- The data generated by AI inference, such as logs and cached context, can be exponentially larger than the final output delivered to the user, especially in workloads like AI video generation.
- McKinsey projects AI inference demand to grow faster than training, making up a substantial portion of future data center demand.
- A majority of IT decision-makers report increased data volumes due to generative AI, with storage costs often outpacing predictable compute budgets.
- Retained data is becoming a valuable asset for future AI workloads, necessitating storage solutions that support dynamic access and faster retrieval.
- Addressing AI data storage requires an architectural shift towards economically scalable capacity that balances performance, capacity, power, and total cost of ownership across the data lifecycle.
- Budgeting for AI infrastructure needs to include storage as a compounding cost factor, not an afterthought, to avoid unexpected expenses.
- Key questions for organizations include defining data retention policies, aligning storage procurement with the data lifecycle, and assessing how storage costs scale with AI usage.