Dell is publicly challenging VAST Data's approach to AI infrastructure, arguing that VAST's storage-embedded design consumes far more power and rack space than Dell's PowerScale. Dell claims PowerScale delivers equivalent performance with 72% less power and 80% less rack space, and contends that VAST Data—despite calling itself a database—lacks the SQL and governance maturity of true enterprise databases. The dispute reflects a key tension in AI Factory design: whether to integrate storage tightly with compute or keep them modular and interoperable.
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Dell's competitive intelligence director published three blogs comparing PowerScale storage to rivals. He claims PowerScale used 72 percent less power, 80 percent less rack space, and 8x fewer backend switches than competitor reference designs—and 41 percent less power and roughly 2x less rack space than VAST Data specifically, based on Nvidia reference designs.
Why it matters
As companies build AI Factories (large-scale AI infrastructure), storage and power constraints are becoming real bottlenecks. Dell argues that storage-embedded AI systems like VAST Data's require more backend network switches and cooling before a single GPU even powers on, making them costlier to deploy at scale. For IT buyers, this means the choice of storage layer directly affects datacenter facilities budgets, not just performance.
What to watch
Dell claims VAST Data positions itself as a database but lacks the SQL optimizer, query planning, and governance that enterprises expect from Snowflake or BigQuery. Dell is positioning its own stack around open standards (Apache Iceberg, Databricks, Snowflake compatibility) rather than proprietary architecture. VAST Data declined to comment on the blogs.
Dell's three-blog salvo signals intensifying competition in the AI infrastructure market, where VAST Data has emerged as a high-profile challenger. The dispute centers on a fundamental architectural choice: whether AI storage should be tightly integrated (VAST's approach) or loosely coupled with open standards (Dell's bet). Power and rack space are not abstract metrics—they translate directly to real operating costs and facilities constraints. As organizations discover that naive Nvidia-based AI Factories can hit power supply and space ceilings in production datacenters, the question of "which storage layer" becomes mission-critical. Dell's framing of this as a hidden cost—"the power budget being spent on the storage layer we chose"—suggests that storage vendors' performance marketing has masked the true total cost of ownership.
Dell also attacks VAST Data's categorical claim: calling itself a database while, according to theCUBE and NAND Research, lacking the SQL optimizer and governance enterprise data teams depend on. This is a deeper positioning critique than efficiency numbers alone. By contrast, Dell emphasizes interoperability with established analytics tools (Databricks, Snowflake, Apache Iceberg), allowing buyers to preserve their existing data stack rather than requiring migration. The five evaluation questions Hyde poses—around data gravity, sync-job labor, GPU utilization, facilities footprint, and analytics compatibility—reframe the buying conversation away from benchmark specs toward real-world operational burden. VAST Data's silence on these claims suggests Dell has struck a competitive nerve, though the absence of direct rebuttal leaves space for VAST to challenge Dell's benchmark methodology or claim context.
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