A private ChatGPT is the phrase a lot of buyers reach for when they mean something more specific: an AI assistant that answers only from company content, respects who is asking, and can show its work. None of that is what “ChatGPT” describes. ChatGPT is OpenAI’s own product, and a business licence for it does not change what the underlying assistant is allowed to see or how it decides what to retrieve.
A governed large language model (LLM) deployment is one where identity, data handling, and audit logging are enforced as part of the system, not bolted on afterwards. That distinction, not the model itself, is what a private ChatGPT for company use is actually supposed to deliver, and the market is already buying against it.
According to Grand View Research, the global LLM market is projected to reach $35,434.4 million by 2030 with a compound annual growth rate of 36.9% while cloud LLM market is set to grow at the highest pace, the on-premise LLM market accounted for the largest share of 2024 revenue despite its slower growth rate.
| Factor | A personal ChatGPT account | A suite-bundled assistant | A DIY open-source build | A governed private platform (Vaultiscan) |
| Where it runs | OpenAI’s public cloud | The suite vendor’s tenant | Infrastructure you provision | Your infrastructure or a sovereign cloud region you choose |
| What it answers from | The open web, plus whatever a user pastes in | Content inside that one vendor’s estate | Whatever your team connects | Your permissioned content, and nothing outside it |
| Permission enforcement | None; single-user context only | Often inherited from the suite’s own access list at index time | Whatever your engineers build | Checked per query, against the requester’s actual permissions |
| Audit trail | Personal usage history only | Product usage logs | Whatever your engineers build | Retrieval-level, source-linked, reconstructable months later |
| Data used to train the vendor’s model | Governed by consumer terms unless disabled | Typically excluded at enterprise tier | Not applicable, you own the model | Never leaves your boundary |
Five properties separate a governed LLM from an assistant with a business licence attached. None of them come from the model.
The system checks the requester’s actual permissions at the moment of the question, not against a flat index built once at crawl time.
Where the data sits, and whether it is used to train anything outside your organisation, is a deployment decision, not a setting you hope is on by default.
Every answer traces back to a specific source document, so a reader can check it rather than trust it.
Who asked what, what was retrieved, and when it happened all need to be reconstructable, not just logged in aggregate.
The large language model (LLM) underneath is a commodity that improves and gets swapped out. The governance layer above it is what actually gets kept.
The use case for using ChatGPT for enterprise data is stalling at the first hurdle: Most suite-bundled assistants are fluent in one vendor’s estate — and the documents that have actual regulatory or contractual liability (contracts, claims files, engineering specifications, and supplier agreements) tend to live in a completely different system.
The EU AI Act came into force on 1 August 2024 and became fully applicable on 2 August 2026, with the high-risk requirements to be staged in from 2 December 2027, tackling the sensitive use cases of biometrics, critical infrastructure, education, and employment, and from 2 August 2028, for AI integrated into products. It is not a suggestion; it’s a fixed sequence. It assumes an organisation can already demonstrate who responded to a system, how, and from where — and that is exactly the gap a governed platform is built to close.
A private LLM is the model layer only: weights running on infrastructure you control, with no retrieval, permission enforcement or audit logging attached by default. That is a legitimate answer to “what is a private LLM,” but it is not what a private AI vs public AI comparison usually implies. A model you host yourself with no permission layer on top is still, functionally, a flat index with better manners; the gap is everything in the section above.
This is where a DIY build tends to underestimate the work. Retrieval against one content source is a demo. Keeping permissions correct as roles change, making a withdrawn document actually stop being retrievable, and answering an auditor in minutes rather than weeks is a different project, and it is where half of enterprise AI projects stall. Documents that drift out of sync with their source, or get summarised without a way to trace the summary back, create the same document corruption problem enterprise AI leaders are now being warned about.
Rather than being a speeded up version of the first three, Vaulti GPT is designed to be the fourth: it responds to only permissioned content, and Vaulti Lake contains the source, version, and scope of access for each retrieved item, making an audit question a query, not an investigation.
It’s shorthand for a governed LLM assistant, not OpenAI’s product running on your own servers. It describes what buyers want (an assistant answering only from company content, with permissions and an audit trail enforced) rather than a specific product.
No. A private LLM is the model layer alone. A governed LLM on top of all this is what makes it usable and defensible for a business: Identity-aware retrieval, data residency controls, source linked answers, and audit logging.
Model inference is the cheapest part of either option. The cost that varies is the connective work: syncing permissions as roles change, propagating deletions, versioning content, and logging retrieval well enough to survive an audit. Price both options on that basis, not on inference alone.
Typically, 6–12 weeks, depending on how many sources need permission-mapping. Don’t expect a single go-live moment — permissions and retrieval need to be verified before anything ships.
Yes. To keep indexing, retrieval and generation within these confines, deployment within your own infrastructure or a selected sovereign cloud region is becoming an expectation that is taken for granted in more and more EU, UK and Gulf markets.
Calling something a private ChatGPT for company use answers the wrong question. The model was never the issue for argument – it’s replaceable, and it will be cheaper next year, no matter who uses it.
What remains unchanged is the layer that determines who asks the question, what they’ll be shown, and whether the answer can be verified against the source. Develop the layer once, on infrastructure you actually control, and it will continue to pay off long after you’ve replaced the model underneath it.
See how Vaultiscan builds a governed AI assistant on your own content → Talk to our team.
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