How to Build a NAND-Resilient AI Storage Architecture
DDN, Tuesday, January 20th, 2026
For years, all-flash became the default choice for AI storage. It simplified decisions and removed performance risk when NAND pricing was stable and supply was predictable. That environment no longer exists.
As NAND prices rise and availability tightens, AI teams are being forced to rethink how storage is designed-not to lower standards, but to match performance to how AI workloads actually behave.
Why 'All-Flash Everywhere' No Longer Scales for AI
All-flash solved real problems in traditional infrastructure. But AI workloads are not uniform-and not every stage of the AI pipeline benefits equally from flash.
Training, inference, preprocessing, checkpointing, and data retention place very different demands on storage. Treating every dataset as latency-critical increases NAND consumption without improving end-to-end outcomes across the AI workflow.
In today's market, that approach creates unnecessary cost exposure-without guaranteeing better performance.