VAST vs Pure vs NetApp vs Dell: Which Storage Platform for AI?

Certification is table stakes, not a decision

NVIDIA certifies storage for its DGX SuperPOD reference architecture, and VAST, Dell PowerScale, NetApp and Pure's FlashBlade//S all appear among the certified platforms. Blocks and Files, NVIDIA SuperPOD storage certification; Everpure, FlashBlade//S certified for NVIDIA DGX SuperPOD. That tells you each can keep a large GPU cluster fed under NVIDIA's test conditions. It does not tell you which will suit your data, your team or your budget, and most enterprise AI estates are far smaller than a SuperPOD anyway.

The questions that separate them are more practical. Is your AI data mostly huge unstructured files and objects, or does it live alongside the block and file data you already run? Are you building a dedicated AI cluster, or adding AI to an existing estate? And how much do you want to depend on products launched in the last eighteen months? Our guide to the right storage platform for AI covers what each stage of the AI pipeline demands. This page is about the four vendors.

How they compare for AI

The honest read on each

VAST Data

VAST was designed for exactly the problem large AI creates: enormous volumes of unstructured data that must be read fast enough to keep expensive GPUs busy. It is widely deployed under large AI and GPU cloud environments for that reason, and for that kind of estate it should be on the shortlist and may well win. Above the storage, VAST has added database, data processing and what it now calls an AI operating system. Those layers are ambitious and moving fast, so judge them separately from the storage, and make sure you are not paying for a platform when you need a store. Our independent look at VAST goes further.

Pure Storage, now Everpure

Pure built its name on simplicity, and its rebrand to Everpure did not change the products. FlashBlade is its fast file and object platform for unstructured data, and FlashBlade//S is certified for SuperPOD. For the largest AI factories, FlashBlade//EXA separates metadata from data so each can scale independently, using standard protocols and off the shelf data nodes. Pure Storage, FlashBlade//EXA announcement. Pure's strength for most enterprises is that AI storage arrives with the same easy operation and Evergreen subscription as the rest of its estate. EXA is the newer architecture, so test it on your own workloads. More in our independent look at Pure.

NetApp

NetApp's case for AI is that your data is already on ONTAP, with the snapshots, replication, security and cloud reach that come with it, and that moving it to a separate AI silo has its own cost and risk. In October 2025 it added AFX, a disaggregated version of ONTAP that scales performance and capacity separately, and the AI Data Engine, which extends ONTAP into the AI data pipeline. NetApp, AFX and AI Data Engine announcement. Both are new. Mainstream ONTAP is excellent at data management but has not been a throughput specialist, which is the gap AFX is designed to close. More in our independent look at NetApp.

Dell

Dell brings the broadest portfolio and the deepest services reach of the four, and can supply the servers, networking and storage for an AI build as one programme. PowerScale is its scale out file platform for large unstructured data, and Dell has added a parallel file system, Lightning, built on PowerScale's operating system, to compete for the most demanding AI throughput. Blocks and Files, Dell Lightning File System; Dell, storage strategy for the AI era. Lightning only became available in April 2026, so ask for references at your scale. More in our independent look at Dell.

What to test before you choose

1. Your data, not the vendor's benchmark. Run a proof of concept on a representative sample of your real datasets, file sizes and access patterns.
2. Data reduction on AI data. Images, video, audio and already compressed training sets often reduce far less than general enterprise data. Size on usable capacity at a realistic ratio, and put the ratio in the contract.
3. Checkpointing and restart. Training jobs write large checkpoints and must restart quickly after a failure. Test write performance, not just reads.
4. Growth without a forklift. Ask exactly how the platform scales from your first deployment to three times the size, and what that costs.
5. References at your scale. Especially for the newest products, speak to a customer running something like your workload in production.

How AI storage is bought, and what to weigh

All four sell outright and on subscription, and the subscription terms matter more than the headline price: what is included in the service, how upgrades are handled, and what happens to price at renewal. AI estates also grow unevenly, so negotiate the price of the next increment of capacity and performance now, while you still have alternatives, rather than at the point you are out of space. The same disciplines from our guide to buying enterprise storage apply, with the added catch that AI data rarely reduces as well as the quote assumes.

Our position

We have no storage product of our own and we work across the major vendors, so our job is to find the one that fits your AI work and then get it on the right terms. Sometimes that is a new dedicated platform. Often it is extending what you already run, which is cheaper and lower risk than it sounds.

Choosing storage for an AI build?

Tell us what the AI workload is, roughly how much data, and what you run today. We will tell you which of these platforms fits, whether your existing estate can do the job, and what it should cost. We spent years on the vendor side selling exactly these platforms.

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