Public, private, and sovereign AI all promise control. Most enterprises are already running more than one — the question is whether that’s by design or by accident.
A joint educational white paper by NetApp® and Iterate.ai.
As of early 2026, 67% of enterprise AI workloads run in public cloud, 22% run hybrid, and 11% run entirely on-premises (industry surveys, 2026). That split isn’t a snapshot of one decision. It’s the average across dozens of them — the typical enterprise now runs 4.2 AI models in production, more than double the 1.9 it ran in 2023 (Gartner).
Every one of those models had to be deployed somewhere: rented from a public provider, built on infrastructure the company owns, or — increasingly — built by a government treating AI compute the way it treats highways and power grids. Renting, owning, and building your own city are three different bets. Most enterprises are placing all three at once, often without a framework for which workload belongs where.
Public AI is a model and a service, developed and hosted by a third party, made available to any organization willing to pay for access — usually through an API. It’s the fastest way to get from “we want AI” to “we have AI,” and for a huge share of use cases, that speed is exactly the right trade.
Like renting a furnished penthouse: you move in today, someone else handles the plumbing, and you get the building’s amenities on day one. But the lease is the landlord’s, the rules are the landlord’s, and you own nothing when you move out.
Every advantage of renting comes with a matching trade-off. None of these are hypothetical — they’re the standard terms of the lease.
This is the same asymmetry The Oohs, Awes, and Dangers of AI Memory (elsewhere in this series) covers in depth: on shared infrastructure, the value of what you reveal doesn’t stay yours. It flows to whoever owns the model.
Private AI runs entirely within infrastructure an organization owns or exclusively controls — on-premises, in a dedicated private cloud, or both. The enterprise decides the model, the training data, the deployment, and the lifecycle. Nothing leaves the building unless the organization chooses to send it.
None of this is free. Building Private AI yourself typically means:
Every challenge on the previous page is a challenge of building Private AI from scratch — not of owning it. That distinction is the entire premise of the AIPod Mini: NetApp storage and Iterate’s Generate reasoning layer, pre-integrated, so a customer gets the ownership of Private AI without first becoming an infrastructure company.
For the deployment mechanics in detail, see From Empty Infrastructure to Working AI in 20 Minutes; for the cost case, see The Rest of the Invoice — both elsewhere in this series.
Sovereign AI takes the same ownership logic to the scale of a nation: compute, models, and data infrastructure built and controlled within a country’s own borders. In 2024 it was an aspiration. By 2026, it’s a budget line in most of the G20.
France’s commitment funds Mistral Compute — 18,000 NVIDIA Grace Blackwell chips in a single facility. The UAE’s Mubadala fund alone deployed $12.9B in 2025. Singapore, India, and the EU (via the AI Act and domestic-model initiatives) are running comparable, if smaller, programs. The logic is the same one driving enterprise Private AI: don’t rent your intelligence from someone else’s infrastructure.
The lesson isn’t about geopolitics. It’s that ownership isn’t a function of budget size — it’s a function of what you actually control in the stack. A nation can spend $100 billion and still depend on foreign chips and foreign partners. An enterprise can own its AIPod Mini outright for a fraction of that and control the entire stack end to end.
The three models trade off against each other on nearly every dimension that matters to a buyer. None is universally “better” — each is the right answer for a different workload.
Note the deployment-speed row: Private AI’s traditional weakness — weeks to months of build time — is exactly what a turnkey appliance like the AIPod Mini is built to close.
Don’t choose a deployment model for the company. Choose one for each workload. Six questions do most of the work.
22% of enterprise AI workloads already run hybrid, and IDC finds 64% of infrastructure-maturity leaders describe their overall environment the same way (Feb 2026). Hybrid isn’t a failure to choose. For most enterprises, it’s the correct architecture.
The hardest part of hybrid isn’t deciding — it’s avoiding data silos once workloads are split across three architectures. NetApp’s unified data layer moves data across on-premises, private cloud, and public cloud without duplication or lock-in; Iterate’s Generate runs consistently on top of it, wherever a given workload lives.
Public, Private, and Sovereign AI aren’t competing answers to the same question — they’re the right answer to three different ones. The mistake isn’t picking the wrong model. It’s picking one model for every workload, when the workloads themselves are telling you which one they need.
A joint educational series on private AI and the AIPod Mini. NetApp — the governed data-control layer. Iterate.ai — the private intelligence layer.
Plain-language definitions for the deployment terms in this paper — enough to hold the conversation with a technical or procurement team.