Most AI Is Shared, Not Private

What You’ll Learn

•The three categories of AI provider — and what each one keeps

•Why the middle column, products sold as “private AI,” is the one to watch

•The four rows where private-sounding and actually-private pull apart

•What you give up, and what you keep, across free, contract-private, and owned AI

There are three categories of AI providers. Many providers offer a free AI in exchange for training that AI on all the consumer usage. Others offer some levels of segmentation in a “private” cloud or a promise to not train on user’s data. Only one type of provider keeps the model, the hardware, the stored data, the memory, and everything the system learns under your control. Very few providers can operate in the first column.

	True Private AI	“Private” cloud & vendor-managed	Public AI / shared
Where it runs	Entirely on machines you own	Vendor's models, partly walled off hardware	Shared models and shared data on shared chips
Who controls the data	You do, completely	Depends on the vendor	You don't
Cost per question	None — you own the box	Per token, plus egress fees	High and hard to predict
Works with no internet	Yes, can be fully air-gapped	No, connection required	No, connection required
Do you own the model	Yes, outright	No, vendor's models only	No, shared with everyone
Can you customise it	No limits	Only what the vendor allows	Whatever the API exposes
Protection for know-how	Contract and technical controls	Limited promises	No real guarantees
Models tuned to your work	Yes, deeply	Limited	Barely
Does your know-how compound	Yes — it stays and grows with you	Only what the private layer retains	It transfers to the model's owner
Attack surface	Small	Mixed — part shared, part private	Large shared systems
How hard to leave	Easy — it is self-contained	Hard — real lock-in	Very hard

Full capability Partial, or with conditions Not available, or outside your control

NetApp | Iterate.aiMost AI Is Shared, Not Private · 01 / 02

How to read the chart

The middle column is the one to watch. It is where most products sold as “private AI” actually sit. In the marketing material, it looks private. Underneath, not so much.

The difference shows up in the last four rows. Whether the system can be tuned to how you work. Whether what it learns stays with you. How wide the attack surface is. And how hard it would be to walk away. Those are the rows that decide what you own in three years.

True private

More to stand up at the start. In exchange, the model, the machines and the compounding all stay yours.

“Private” cloud

Better isolation and a private-sounding contract. The model is still the vendor's, so the learning still ends up on their side.

Public / shared

Fast to start, nothing to run. You give up control of the data, the cost, and everything the system learns from you.

Where AIPod Mini sits

AIPod Mini is built for the first column. NetApp^® supplies the governed storage, Iterate.ai supplies the model layer, and both run on hardware the customer owns — so no question has to leave the building to be answered.

Check the last four rows yourself

Nearly every supplier will use the word private. Very few can put a check in all eleven rows. Ask which column they are in, then check the last four rows yourself.