VAST Becoming Core AI Data Platform

This reveals that VAST is trying to become infrastructure that clouds standardize on, not just a storage vendor they buy from. The reason is simple, VAST is packaging storage, metadata, database, and data processing into one system for AI workloads, which is much harder for a cloud to assemble from separate parts than a normal file store. That is why the company sells into million dollar deployments, and why cloud providers and GPU clouds are treating it like a core layer of the AI stack rather than an accessory.

• The product is broader than storage. A team can keep raw files, query them with SQL, search metadata, and run Spark or Python jobs on the same platform. That matters for clouds because AI customers do not want separate systems for object storage, cataloging, vector data, and preprocessing.
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• The closest competition still comes from fragmented stacks. WEKA and DDN focus more narrowly on high performance AI storage, while Pure, Dell, NetApp, and IBM bring legacy enterprise storage. VAST is trying to win by collapsing several budget lines into one purchase, which makes internal builds less attractive even for very large operators.
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• The clearest proof point is cloud distribution. VAST says its platform spans AWS, Azure, and Google Cloud through DataSpace, and it signed a $1.17 billion commercial agreement with CoreWeave in November 2025 to serve as the primary data platform for its GPU cloud. That is the kind of role a hyperscaler or neo cloud would only outsource if building it themselves was slower or weaker.
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Going forward, the market is likely to split between basic cloud storage and AI native data platforms that keep GPUs fed and data usable across training and inference. If VAST keeps landing cloud and GPU provider relationships, it moves up from selling boxes and software into becoming a control point for how AI data is stored, queried, and processed at scale.