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NetApp Unveils Novus to Break AI's Metadata Bottleneck

NetApp Inc. has introduced Novus, a unified storage and data management approach aimed at eliminating metadata bottlenecks that hinder enterprise AI deployments. The platform seeks to make fragmented enterprise data usable for GPU clusters, models, and AI agents while addressing governance challenges tied to manually applied policies. The announcement underscores growing pressure on data infrastructure to keep pace with production AI workloads at zettabyte scale.

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NetApp Inc. on Monday introduced Novus, a unified storage and data management platform designed to eliminate metadata bottlenecks that slow enterprise AI workloads. The announcement, reported by SiliconANGLE, positions the platform as a bridge between intelligent data infrastructure and production AI, targeting organizations struggling to make fragmented enterprise data usable for GPU clusters, large language models, and autonomous agents.

The company said fragmented data and governance frameworks built around manually applied policies are preventing enterprises from realizing returns on their AI investments. Novus aims to address these pain points by consolidating metadata management across storage environments, allowing AI systems to locate, access, and govern data at scale without the latency penalties that typically accompany sprawling, siloed repositories. NetApp framed the launch as a response to infrastructure demands that now stretch to zettabyte-scale data volumes.

The move places NetApp in direct competition with other storage and data management vendors racing to serve AI factories and hyperscale GPU deployments. If Novus delivers on its promise, it could reduce the operational friction that has kept many enterprise AI pilots from reaching production. Industry observers will watch for customer adoption metrics and whether NetApp can demonstrate measurable throughput and governance improvements in real-world AI pipelines.

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