Why AI infrastructure planning must account for data storage

Enterprise investment in artificial intelligence has focused heavily on compute, including access to GPUs, model performance and inference costs. But data storage is emerging as another strategic part of AI infrastructure, according to David Boland, vice president of cloud strategy at Wasabi, writing for TechRadar.

AI workloads rely on data throughout their lifecycle. Organizations collect and prepare datasets, make training data available to compute, retain checkpoints and model artifacts, and may move information between infrastructure providers. Different stages call for different storage priorities: active data may need high performance, while historical datasets and model artifacts may place more emphasis on durability, security, access and cost.

Boland describes “AI storagemaxxing” as systematically optimizing storage across that lifecycle so infrastructure matches business and workload needs. He argues that using one storage approach for every stage can lead to inefficient infrastructure use.

Moving workloads between hyperscalers, specialized GPU providers and newer cloud platforms can be attractive as availability and economics change. However, large datasets and model artifacts can be costly and operationally complex to transfer. Boland says data mobility should be considered during infrastructure design, and that open standards and interoperable storage can help separate data from compute.

He also says storage resilience belongs in AI security planning. Immutable copies, access controls and recovery processes can help protect data from ransomware, accidental deletion and unauthorized changes. Storage planning should account for capacity, access, retrieval and transfer costs, as well as the needs of production workloads over their full lifecycle.