VAST as Portable Data Layer

This makes VAST less like a supplier to any one GPU cloud and more like a portability layer that follows the workload. The practical point is that model builders and enterprises can train in CoreWeave, run inference in a hyperscaler, or move sensitive jobs on prem, while keeping the same storage, database, and data orchestration layer underneath. That reduces platform lock in for customers and makes cloud consolidation less damaging for VAST.

• VAST already frames its product as one abstraction layer across Amazon, CoreWeave, and on prem environments through DataSpace. That means a customer can keep one data namespace and one software stack even if compute capacity shifts to a different provider.
  1 sacra
• The company is moving from selling storage into selling a bundled software layer. In the interview, the mix flipped from about 70% storage only two years ago to about 70% bundle today, which makes the relationship stickier at the application level, not just the hardware level.
  1 sacra
• This is the same logic behind VAST's cloud partnerships. CoreWeave and other AI clouds act as distribution, because they bring in startups and enterprises that need GPUs fast, but VAST is trying to become the standard data layer those customers keep using if they later move to another cloud or back into their own data center.
  2 sacra 3 sacra

Going forward, the winners in AI infrastructure will be the software layers that let workloads move as GPU supply, pricing, and regulation change. If VAST keeps embedding itself between hardware and applications across neoclouds, hyperscalers, and sovereign deployments, cloud churn will expand its reach instead of breaking its customer base.