If you are evaluating an AI data platform this year, you have probably noticed how similar the shortlists sound. In vendor meetings, the same terms keep coming up: disaggregated, unified, open, intelligent and no lock-in.
NetApp describes a platform that is “disaggregated and intelligent from the beginning so that storage, services and control scale independently without lock-in.”¹ Everpure describes an Enterprise Data Cloud built on a unified data plane and intelligent control plane.² VAST describes an open ecosystem that gives organizations “the flexibility to choose the technologies that best meet their performance, operational and business requirements.”³
Each description is directionally reasonable. None helps you choose. The language has converged ahead of the engineering, so shared vocabulary no longer tells you how a platform will perform in your environment. The differences are still real; they have moved into the documentation.
The more useful question is narrower: Which architecture fits the environment you already have, minimizes disruption and preserves your ability to choose later? Published documentation can help answer that question today, across five lenses.
Lens 1: Data locality, or where AI can run
Enterprise data has gravity. It sits across core data centers, edge sites, sovereign regions and software-as-a-service environments, held there by regulation, latency and economics. Most of it will never move. Every architecture on your shortlist should reflect that reality.
NetApp’s initial AI Data Engine (AIDE) vision appeared to put the AI processing destination first — AIDE runs as an ONTAP-integrated service on AFX and NetApp’s architecture documentation described synchronizing remote ONTAP data to a local AFX cluster for AIDE processing, potentially requiring customers to replicate data onto a dedicated AI platform rather than bringing processing to each dataset.
That characterization now needs to be updated: NetApp announced the launch of AIDE in March 2026²² and after acquiring DataPelago in July 2026, it began positioning DataPelago’s Nucleus engine as a way to perform GPU-accelerated, zero-copy processing where data resides.²³ NetApp says it will align Nucleus with AIDE and AFX,²⁴ but also describes that integration as an ongoing effort, making the acquisition look less like proof that the original architecture solved data gravity and more like a strategic correction toward in-place processing.
Dell’s AI Data Platform starts from that reality: data has gravity, so an AI platform must meet data where it is rather than ask customers to move it into another bespoke data silo.
VAST’s model differs in kind. VAST DataSpace presents a global namespace across distributed VAST clusters. The distinction that matters is not centralized versus distributed; it is that everything in that namespace must be VAST. As NAND Research put it, while most major storage vendors build open, modular ecosystems, VAST delivers “a tightly integrated, opinionated stack where VAST controls the full experience.”⁷
Everpure offers the strongest counterpoint here, and it deserves credit. Everpure Data Intelligence, built on the 1touch.io acquisition, operates across on-premises infrastructure, public cloud, software-as-a-service applications and third-party storage — not only Everpure arrays.⁸ That is genuine heterogeneous reach at the discovery layer. But discovering and classifying data in place is not the same as processing and serving it in place. The data plane underneath is still Everpure’s.
Dell’s AI Data Platform starts from the opposite premise: the environment is already distributed and will stay that way. Its federated control plane queries across file systems, object stores, warehouses and clouds in place — including storage Dell does not own — without requiring a central copy first.⁹ PowerScale, ObjectScale and Lightning File System are where many customers land for performance and economics, not a precondition for using the platform.
Lens 2: Onboarding effort, or the sync relationship that never ends
Reducing copies is not the same as eliminating them. NetApp’s AIDE documentation describes Data Sync using SnapMirror to replicate data from remote ONTAP clusters to the local AFX cluster for processing. It also describes FlexCache as a way to cache read data from an origin volume.⁵ Both are legitimate optimizations. Both are also data-movement mechanisms, which makes the synchronization relationship permanent rather than a migration phase.
VAST’s SyncEngine plays a similar role for data created outside VAST. NetApp’s July 2026 acquisition of DataPelago and its Nucleus engine points in a different direction: “zero-copy activation” that processes data where it lives rather than replicating it. However, NetApp’s announcement includes a Statement of Product Direction that disclaims any commitment to deliver the capability. How — or whether — it changes the synchronization relationship remains undocumented as of publication.²⁰
When AI runs inside a namespace, everything outside that namespace becomes an operational obligation that someone owns indefinitely. That is disruption without a cutover date. Dell’s federated control plane avoids that standing obligation: it queries distributed file systems, object stores and clouds in place, so there is no permanent synchronization relationship or background replication to own.⁹
Lens 3: Pipeline acceleration, or keeping GPUs fed
Dell doesn’t lead with speed claims, and the benchmark landscape keeps moving anyway. Competitors, by contrast, do lead with speed: Everpure holds the official SPEC Storage AI_Image record with FlashBlade//EXA at 6,300 concurrent AI jobs, compared with a previous high of 5,000.¹⁰ VAST, testing with AMD Instinct MI355X, reported a nine-times improvement in time to first token using VAST for KV cache offloading.³
Acceleration matters most where the pipeline is slowest, which is rarely the final step to the GPU. Dell applies NVIDIA CUDA-X libraries, cuDF and cuVS inside the data engines themselves, reporting up to three-times faster data processing, up to 12-times faster vector indexing and a 19-times reduction in time to first token compared with CPU-based workflows.⁹ These are vendor-reported figures.
The architectural point is the one that lasts: acceleration you can apply only after ingestion carries a precondition. The data must first be moved in. That is more of your existing environment changing to fit the platform.
Lens 4: RAG and search flexibility, or whose engines win
This is the lens many evaluations skip, and it is where lock-in can surface. VAST supports open standards, including Apache Iceberg. But as NAND Research observed, “the underlying database engine is VAST-written.”⁷ Supporting a format and working with the engines your data team chose are different commitments.
Most enterprises did not choose Databricks, Snowflake or Trino by accident. An architecture that requires them to revisit those choices, or to accept a vendor-specific reimplementation in place of the standard, open engine, asks for more than a storage decision.
Dell’s approach runs the other way: Iceberg and Delta Lake as open table formats over ObjectScale, with analytics on Starburst, processing on Apache Spark and search on Elastic. That allows the existing stack to remain a first-class citizen rather than becoming a migration target.⁹,¹¹,¹² These are the same industry-standard engines your data team already runs, not vendor-specific rewrites of them, so existing skills, queries and integrations carry over.
Ask all four vendors which parts they build and which parts they source from partners. Everpure Data Stream was announced in beta at GTC 2026 and co-engineered with Supermicro.¹³ VAST relies on hardware and integration partners for full-stack delivery.¹⁴ NetApp’s story spans ONTAP, AFX, AIDE and a recently acquired processing engine whose integration path is not publicly detailed.¹,⁵
None of that disqualifies anyone. But “Who is accountable when the pipeline stalls?” has a different answer in each case. For Dell, that answer is one vendor accountable across servers, networking and storage, integrating open, standard engines a customer could swap rather than a proprietary core.
Lens 5: Operational overhead, and what it costs
The least glamorous lens is often the most predictive. VAST’s PolicyEngine and TuningEngine, which complete its agentic story, are slated for release by the end of 2026.¹⁵ Several Everpure Intelligent Control Plane capabilities have stated availability in the fourth quarter of 2026.²,⁸ Dell’s position in this lens is what it ships: the Dell AI Data Platform’s data engines and federated control plane are available now and managed as one operational model, not assembled from capabilities still dated to a future quarter.⁹
Those are near-term dates, but the platform you buy this quarter is the platform shipping this quarter. The gap between announced and available is where AI programs quietly lose quarters. NetApp’s documentation still directs customers to the AIDE Console, ONTAP System Manager and the ONTAP CLI for different tasks.⁴,⁵
Expect more vision in this window. Everpure laid out its long-term strategy at its September 23 Financial Analyst Meeting, and NetApp opens INSIGHT the day after this post. Weigh whatever either company describes there against what its shipping release supports today.²¹
This is where cost belongs: in the architecture
I won’t put a three-year savings percentage on this. Any figure derived from public information would break down against the specifics of your environment, so the defensible claim here is architectural, not a number. Every forced copy is storage you bought twice. Every synchronization relationship is a job someone owns. Every control plane is a governance model someone must reconcile. And an architecture that assumes all data belongs on flash — as both VAST’s end-of-the-HDD-era positioning and Everpure’s all-flash portfolio do — removes the media lever exactly when NAND pricing makes it most valuable.¹⁶ That is disruption paid annually.
What lower disruption looks like in production
CTBC Bank, one of Taiwan’s largest financial institutions, built AI-powered fraud prevention on the Dell AI Data Platform, monitoring transactions across more than 7,500 ATMs in real time with a 0.03-second detection response time and helping prevent the loss of more than US$60 million in customer assets in 2025.¹⁷
Dell also runs its own multi-petabyte analytics environment on the platform, querying data in place rather than building extract, transform and load pipelines.¹⁸
Neither example is a story about the newest architecture. Both are about activating data where it already lived.
What I would ask before signing
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- If data won’t live in your platform three years from now, can the release you’re shipping query or access it in place today — or is that still on the roadmap?
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- Which replication or background-sync mechanisms bring that data in, and who owns those jobs when schemas change?
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- Where is acceleration applied in the pipeline, and does the data have to be fully ingested before it takes effect?
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- If we keep Databricks, Snowflake or Iceberg on our terms, what breaks? Are the engines based on standard, open technologies or vendor-specific re-implementations?
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- Which console does each task actually happen in? If our AI strategy changes in year three, what does the exit look like?
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These aren’t gotcha questions. They separate an architecture from a hypothesis. Every vendor here — including mine — should answer them from its documentation.
The best architecture isn’t necessarily the newest one. It is the one your existing environment can adopt.
See Dell AI Data Platform in production
Join us Oct. 6–7 for the Dell AI Data Platform Event: From Ambition to AI at Scale, broadcast live on theCUBE. Enterprises and neoclouds will show how they run AI on the data they already have, wherever it lives.
Dell reported this
Source: www.dell.com
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