Private AI infrastructure vs ChatGPT Enterprise
ChatGPT Enterprise is not the same product as the free or Plus version of ChatGPT, and it's worth being precise about what it actually protects against before deciding whether it's enough for your organization. The short version: it protects you from OpenAI using your conversations to train future models. It does not change the fact that OpenAI, a third party, still receives and processes every prompt you send. Whether that distinction matters to you depends on what you're putting in the prompts.
What ChatGPT Enterprise actually does
OpenAI states that ChatGPT Enterprise conversations are not used to train its models by default, and the plan adds admin controls: SSO, domain verification, usage analytics, and the ability to manage seats and workspace membership centrally. Encryption in transit and at rest is standard. For a company that mainly needs to stop individual employees from pasting sensitive text into a personal, unmanaged ChatGPT account, Enterprise genuinely fixes that specific problem. Check OpenAI's own enterprise page for current terms and pricing, since both change and we won't guess at a per-seat number here.
What it doesn't do is remove OpenAI from the path. Your prompts still leave your network, hit OpenAI's infrastructure, get processed by a model OpenAI operates, and the response comes back over the same path. OpenAI's no-training commitment is about what happens to the data after that point, not about whether the data left your control in the first place. For most contract and compliance purposes those are different questions, and it's worth reading Enterprise's terms carefully rather than assuming "no training" means "no exposure."
What changes with a dedicated Spark
A GPUwerk Spark is a dedicated NVIDIA DGX Spark, 128GB of unified memory, that you provision to run models you choose, on infrastructure that no other tenant shares. Prompts go from your application to that machine and nowhere else. No third party's terms of service govern what happens to them, because no third party is in the loop. At $0.79/hour for a single node or $1.79/hour for a two-node cluster, the ongoing cost is straightforward to reason about, and there's no seat-based pricing to negotiate as headcount changes.
The tradeoff is real and worth stating plainly: you're now responsible for model selection, for keeping the serving stack running, and for the fact that no single open-weight model matches GPT-4-class output on every task out of the box. OpenAI has spent enormous resources on both the base model and the product layer around it, chat memory, file search, code execution, and a self-hosted setup starts without any of that unless you build it. See our open-weight vs closed-weight models post for how that quality gap has narrowed and where it hasn't.
The actual question to ask
This isn't a case where one option is simply better. It's a case where the two options answer different questions. If your concern is "can our employees be trusted to use a consumer AI tool with company data," ChatGPT Enterprise, with its admin controls and no-training terms, is a legitimate and much cheaper-to-operate answer than building your own stack. If your concern is "can a US company's servers see this data at all," under a specific regulatory regime, a client contract, an EU data residency requirement, a defense or healthcare compliance rule, then no amount of admin tooling on top of a third-party service resolves it, and the dedicated-hardware answer is the one that actually satisfies the requirement.
It's also common to need both, at different tiers of sensitivity. General drafting and internal brainstorming through ChatGPT Enterprise, with its admin oversight, is a reasonable default for most employee use. Customer data, source code under an NDA, or anything under EU data residency rules is a separate bucket that belongs on infrastructure where the answer to "who else can see this" is nobody, provably, because it's your hardware. See our EU data residency post if that's the specific driver.
Where the line usually falls in practice
Legal, healthcare, and defense-adjacent teams tend to draw the line early and treat anything client-identifiable as off-limits for any third-party API, Enterprise tier or not, because their contracts or regulators require data to stay within a defined boundary that a SaaS vendor's terms of service can't satisfy no matter how strong. Startups without those constraints more often start on ChatGPT Enterprise for the product maturity and move specific workloads to self-hosted infrastructure only once a customer, an investor, or a specific deal requires it. Neither path is wrong; the mistake is picking one without having actually asked which of your data categories requires which guarantee.
If you're evaluating whether a private deployment is worth the operational overhead for even a subset of your workloads, our private LLM hosting page has the specifics on what a dedicated deployment looks like day to day.