Enterprises have spent the past three years buying GPUs faster than they can use them. For most AI initiatives, the real bottleneck has shifted from compute to data. Data is fragmented across silos, locked in formats that no model can consume, and ungoverned in ways that make security and compliance teams nervous about letting an AI agent near it.
A recent IDC survey, commissioned by Everpure and published in June 2026, found that 94% of IT leaders cite data quality as the primary factor in determining an AI project’s success.
That gap between AI ambition and data readiness has a price tag. Idle GPU clusters waiting on slow, poorly prepared data have become a capital allocation problem, and boards are starting to ask about it directly. The result is a structural shift in enterprise storage spending.
IDC’s most recent Worldwide Quarterly Enterprise Storage Systems Tracker recorded $9.2 billion in global external storage vendor revenue in the first quarter of 2026, up 22.7% year over year, the market’s fastest growth in years, driven largely by AI-focused platform demand.
That growth signals a shift in what enterprises expect from storage. A decade of enterprise storage competition focused on capacity, latency, and price per gigabyte. The current cycle centers on a different question. Can this platform make an organization’s data usable by AI, safely and quickly, without months of manual preparation? Everpure’s most recent product announcement is the clearest example yet of a storage vendor rearchitecting itself around that question.
(Disclosure: NAND Research provides advisory and other services to Everpure and every other company mentioned in this article)
Inside Everpure’s Bet On Data Readiness
Everpure, the company formerly known as Pure Storage, used its recent Pure Accelerate customer conference to lay out what it calls a “data primacy architecture.” The premise is that decades of application-centric IT design trapped enterprise data and context inside silos built for specific business functions, and that AI now requires unlocking that data at the source rather than continuing to move and copy it between systems.
Two new products embody that strategy:
- Everpure Data Stream, built on the NVIDIA AI Data Platform reference design, automates the pipeline that converts raw, unstructured enterprise data into a form AI models can use. Everpure claims that Data Stream cuts raw data preparation time from months to minutes by replacing manual ingestion with a GPU-accelerated pipeline that runs from ingestion through inference.
- Everpure Data Intelligence, built on technology from its acquisition of 1Touch in May 2026, discovers, classifies, and contextualizes data across an enterprise’s environment, mapping it into what the company calls a universal data relationship graph.
The business implications extend beyond speed. Discovery and classification are compliance functions as much as technical ones, and Data Intelligence pairs its mapping capability with attribute-based access controls designed to keep AI agents from touching data they should not see. Everpure and most of its rivals now argue that this combination, preparation speed plus governance, has become the real product enterprises are buying, with the underlying flash array reduced to a commodity.
Lynn Lucas, Everpure’s chief marketing officer, explained that expanding the scope from traditional storage into broader AI-focused data management is not an easy path to take. Buyer personas shift, as traditional storage buyers aren’t always the ones making decisions about data management tools. Introducing new capabilities without distracting from the core enterprise storage business can also be challenging.
At the same time, she explained, it’s a necessary journey for Everpure. Enterprise data feeds enterprise AI transformation and expanding the capabilities of the platform already storing your data into understanding your data is a natural next step. As she said, this “quiets the anxiety of adopting AI at scale.”
A Crowded Field, With No Consensus Winner
Everpure is not alone in making this bet, nor is it the largest player. IDC’s first-quarter 2026 storage data shows Dell Technologies holding the largest share of the global external storage market at 31.2%, up from 27.1% a year earlier, with revenue up 40.8% year over year.
NetApp holds the number two market position, with Everpure ranking third but growing fastest among the leaders, up 37.9% year over year. The gap between Everpure and NetApp has narrowed to about $100 million in quarterly revenue.
Dell Technologies
Dell Technologies takes the broadest approach to AI data management, folding data infrastructure into a much larger AI Factory partnership with NVIDIA that spans compute, networking, and storage under one commercial relationship.
Its Lightning File System and Exascale Storage platform target the highest-performance segment of the market, including neocloud operators and quantitative trading firms. This is backed by an orchestration and analytics layer built with Starburst.
NetApp
NetApp is playing a different hand, betting that customers will prefer to extend infrastructure they already trust rather than adopt something new. Its AFX architecture and AI Data Engine build directly on three decades of ONTAP, giving existing NetApp customers a lower-friction path to AI readiness. Its recent acquisition of DataPelago will increase NetApp’s capabilities in this space, but the company hasn’t yet made any related product announcements.
Hewlett Packard Enterprise
HPE, meanwhile, has folded storage into a wider AI Factory and GreenLake operating model, increasingly tied to its Juniper Networks acquisition, positioning its Alletra Storage MP systems as one piece of a unified compute-storage-networking stack rather than a standalone decision.
None of the four has solved the hardest version of this problem: governing AI-ready data consistently across a genuinely multi-vendor, multi-cloud enterprise estate. Everpure’s Data Intelligence layer works well within its own storage footprint but competes with far more established data governance vendors, such as Databricks, once an organization looks beyond a single vendor’s hardware.
What This Means For The Next Two Years
Expect the technical differences among these platforms to narrow before they widen. NetApp, Everpure, Dell, and HPE are all building on the same NVIDIA reference architectures for GPU-accelerated data pipelines, which means raw throughput and time-to-first-token claims will converge as each vendor adopts comparable underlying silicon.
The more durable competitive line is forming one layer up, in the governance, cataloging, and semantic tooling that determines whether an enterprise trusts an AI agent to act on its data at all.
Watch for continued price inflation in the underlying storage components. IDC attributes part of the current storage market surge to rising SSD, HDD, and DRAM prices, on top of genuine AI-driven demand, and expects that pricing pressure to persist through 2027 as new fabrication capacity comes online. Enterprises budgeting for AI infrastructure should plan for storage costs to rise independent of any vendor’s specific architecture.
For IT buyers, the practical decision has moved beyond price per terabyte. What matters now is which vendor’s data governance model an organization is willing to build its AI operating model around for the next several years, since that choice, more than any storage benchmark, will determine how quickly and how safely AI moves from pilot to production.
