Hyperscaler Storage Not Built for AI

This is a statement that hyperscaler cloud storage was designed for general enterprise workloads, not for GPU clusters that need thousands of processors pulling from the same data pool at once. VAST’s pitch is that AI breaks the old tradeoff. Clouds have durable object stores and block storage, but AI training, inference, vector search, checkpoints, and agent memory need much higher shared throughput, lower latency, and cheaper scaling across flash, compute, and geographies.

• VAST was built around disaggregated shared everything architecture. In plain terms, storage capacity and performance scale separately, so a customer can add more GPUs or more flash without rebuilding the whole system. That matters for AI clusters where compute demand can spike much faster than data growth.
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• The practical gap versus big clouds is that many cloud storage layers were built for apps writing files or objects independently, not for massive parallel GPU jobs reading checkpoints, embeddings, and training data together. VAST now positions its software as the common layer across AWS, CoreWeave, on prem, and deeper integrations with Google Cloud.
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• This also explains the competitive split. Older HPC storage vendors like WEKA focused on feeding fast compute with parallel file systems and GPUDirect. VAST is trying to go one layer higher by combining storage with a database and event driven data engine, so customers can store files, vectors, and trigger AI workflows in one stack.
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The direction of travel is clear. Public clouds are adapting their lower layers to look more like AI infrastructure, and the winner will be the software layer that lets enterprises move the same data and workflows across hyperscalers, neoclouds, and on prem without rearchitecting each time. That is the opening VAST is trying to occupy.