Your AI Storage Bill Is the Cost No One Is Watching
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AI infrastructure is driving up semiconductor costs, with DRAM prices rising 90-95% QoQ and NAND flash expected to climb 70-75% in Q2 2026, as wafer production shifts toward high-bandwidth memory for GPU accelerators. While cloud provider list prices for object storage remain flat, the decade-long trend of falling storage costs has reversed. Simultaneously, AI workloads generate massive new data footprints — training datasets, vector embeddings, model checkpoints, RAG corpora, and inference logs — compounding storage volume at the worst possible time. FinOps teams focused on GPU spend are overlooking this dual threat. Recommended actions include tracking storage growth alongside GPU on AI dashboards, attributing storage costs to specific workloads and teams, reinstating lifecycle policies and tiering, and updating multi-year forecasts to reflect a flat-to-rising storage cost floor.
