Comparison
Private AI vs ChatGPT Enterprise
We sell one of these, so read this knowing that. We have also told plenty of companies to buy the other one, and the conditions where that is the right call are specific enough to state.
The short answer
Our verdict, including where we lose
If your best AI use case involves documents you are contractually or legally barred from sending to a third party, self-hosting is the only option that works. If it does not, ChatGPT Enterprise is usually faster to adopt, easier to run, and better at open-ended reasoning. Most businesses genuinely have both kinds of work.
Side by side
Where each option actually lands
| Private AI (self-hosted) | ChatGPT Enterprise | |
|---|---|---|
| Where your data goes | Nowhere. Stays inside your boundary | To OpenAI, under enterprise terms that exclude training |
| Open-ended reasoning | Good. Behind the frontier | Better. Frontier models, updated continuously |
| Retrieval over your documents | Strong. Purpose-built, scoped per team | Good, within the platform's structure |
| Cost shape | Capital or fixed infrastructure, near-zero per use | Per seat, per month, grows with headcount |
| Time to first value | Days to weeks | Hours. Buy seats and start |
| Who operates it | You, or us on your behalf | OpenAI. Nothing to run |
| Model upgrades | You choose when to swap | Automatic |
| Audit trail | Yours, complete, with your retention rules | Platform admin logs |
| Works offline / air-gapped | Yes | No |
| Vendor lock-in | Low. Open weights, portable | Higher |
Where ChatGPT Enterprise is genuinely the better buy
We would rather say this clearly than have you find out after paying us.
- Your work is open-ended reasoning. Strategy, complex analysis, hard writing, ambiguous problems. Frontier models are measurably ahead here and the gap is real.
- You have no data constraint. If nothing you want help with is confidential, you are paying for a privacy guarantee you do not need.
- You have nobody to own it. A self-hosted system needs an owner even when someone else maintains it. Managed is honestly better than neglected.
- You need it working this week. Seats can be bought this afternoon.
- Your team is small and stable. Per-seat pricing is fine at fifteen people. It is the growth curve that hurts.
Where self-hosting is the only thing that works
- A contract or regulation forbids third-party disclosure. An NDA, a privilege obligation, a data processing restriction. Enterprise terms promise no training; they do not change the fact of disclosure to a vendor, and for some obligations that fact is the problem.
- The documents are the competitive position. Formulations, tooling specifications, proprietary methods. Vendor policies are good today and subject to change.
- Air-gapped or offline operation. No debate here.
- Headcount is growing fast. Per-seat economics and rapid hiring are an unpleasant combination.
- You need an audit trail you control, with your own retention rules, because someone will eventually ask you to produce it.
The comparison people get wrong
The usual framing is "is the open model as good as GPT-5". It is the wrong question, because it compares against a tool you are not permitted to use for the work in question.
The real comparison is between a self-hosted assistant grounded in your own documents and nothing — which is what most regulated businesses actually have today, because the compliant answer was to forbid AI rather than to deploy it safely.
Against nothing, a well-configured open-weight model doing retrieval over your contracts is transformative. Against GPT-5 on a creative writing task, it is not. Both statements are true and they are about different jobs.
What most businesses end up doing
Both, deliberately, with an explicit routing rule. Confidential retrieval and document work stays inside; general reasoning and drafting goes to a commercial model. The rule is written down rather than left to individual judgement, because "use your discretion about what you paste into ChatGPT" is not a control.
If you want that mapped out for your business specifically, that is what an implementation assessment produces, and it frequently recommends fewer private deployments than people expect.
Questions people actually ask
Before you call
Does ChatGPT Enterprise train on our data?
No — OpenAI's enterprise terms exclude business data from training. That is a real and meaningful protection. It is a different thing from the data never leaving your environment, and which of those two you need depends on your obligations rather than on your comfort level.
Is a self-hosted model much worse?
On open-ended reasoning, yes, noticeably. On retrieval, extraction, summarisation and classification over your own documents — the work most businesses actually want — the difference is small and frequently irrelevant.
Which is cheaper?
Depends on headcount and volume, and the crossover is real rather than rhetorical. Per-seat pricing is cheaper for a small team; fixed infrastructure wins as headcount grows. We model it against your actual numbers in an AI cost analysis, and sometimes the answer is to keep paying per seat.
Want this run against your numbers?
Bring your actual volumes and invoices. If the other option wins, we will tell you.
