ChatGPT Enterprise or Business vs your own AI server: data, features, control and cost

ChatGPT Enterprise vs your own AI server in one table

ChatGPT Enterprise and ChatGPT Business give every employee OpenAI's current models in a managed service, billed per seat, with data handling that OpenAI documents and you accept by contract. An own AI server runs open-weight models, whose files are published for anyone to run, on hardware you control, so prompts and documents stay with you and the cost no longer grows with head count, but the features exist only as far as they are set up and someone has to run the system. Which suits a company depends on the data it wants to use, the features its staff need and how many of them work with AI at the same moment.

OpenAI business pricing page, Enterprise privacy page (updated 8 January 2026), business data page and help centre article on data residency, all read 25 October 2026; own server by our users-per-card rule. Our summary of the provider's pages; the current pages apply.

This comparison is for owners, managing directors and office managers who are offered a per-seat subscription and hear that an own server would be the alternative. It sets out the provider's own statements, dated, and what the server side means in practice, without prices. Our article on private LLM or cloud API covers the different comparison against paying per token.

What OpenAI documents for ChatGPT Business and Enterprise

OpenAI's pages, read on 25 October 2026, describe two business plans. ChatGPT Business is "for teams of 2–200 employees", priced per seat and billed monthly or annually, with a standard and a premium seat type. ChatGPT Enterprise is priced through OpenAI's sales team and, according to the pricing page, adds data residency, SCIM user provisioning (accounts created and removed automatically from the company directory), Enterprise Key Management, role-based access controls, a compliance API and custom retention policies.

Training. "By default, we do not use your business data for training our models," the Enterprise privacy page says (updated 8 January 2026). The business data page adds that the data "remains confidential, secure, and entirely owned by you".

Retention and access. On Enterprise, "your workspace admins control how long your data is retained", and deleted conversations are removed within 30 days unless OpenAI is legally required to retain them. On Business, admins can also set retention, and the page opens access to OpenAI employees who need it for engineering support, investigating potential platform abuse and legal compliance, and to specialised contractors bound by confidentiality and security obligations.

Location. The help centre article on data residency names "new ChatGPT Enterprise/Edu customers" as eligible to have content stored at rest in Europe (the EEA and Switzerland) and in other regions. Eligible customers with residency in Europe can also choose inference in Europe, meaning the answers are computed on GPUs there. The article lists what stays outside that scope: data handled through external integrations "such as Apps & MCP and Web Search", workspace metadata, billing information and logins, and it notes that authentication, routing and CPU-based steps such as PDF text extraction may still occur outside the region.

Contract. For customers in the EEA the agreement is with OpenAI Ireland Ltd, and OpenAI says it can sign a data processing addendum. Its business data page lists SOC 2 Type 2 and ISO/IEC 27001, 27017, 27018 and 27701, encryption with AES-256 at rest and TLS 1.2 or higher in transit.

Whether these terms fit your data is the assessment of your data protection officer. Our article on local AI and GDPR explains which GDPR duties stay with your company in either case.

What an own AI server gives you instead

On an own server the model is a file on your disk. A prompt goes from the employee's browser to your server and back, and no outside company receives it. You decide how long chat histories, logs and the search index keep data, who may read the query log and whether the server has internet access at all. The assistant searches your file shares with the access rights your staff already have, so a document someone could not open before does not appear in their answers.

The other side of that control is responsibility. Someone has to install updates, watch the server, keep backups and answer when it stops. Our Private AI/ML service builds and supports the platform under an agreed SLA and trains your team; without a server room of your own, it runs the platform on dedicated hardware in a Tier-3 data centre in Lithuania, inside the EU.

We build these servers with two, four or eight RTX PRO 6000 Server Edition cards; send us your head count and the tasks your staff use ChatGPT for, and our team replies with a configuration and a pilot plan.

Where open models keep up and where they lag

The subscription gives access to OpenAI's newest models, which are not published as weights. An own server runs open-weight models, and OpenAI publishes one itself: gpt-oss-120b, with 117 billion parameters of which 5.1 billion are active per token, under the Apache 2.0 licence. Its model card lists built-in browsing tools and function calling, and three reasoning levels set in the system prompt. Other publishers release models under their own licences, which our guide to LLM licences for commercial use compares.

For drafting, summarising, translating and answering from company documents, the question is how an open model does on your own tasks, and a test with real questions answers it; our comparison of local LLM and ChatGPT answer quality shows how to run one. The larger difference is in product features. The Enterprise page names agents, connectors to company tools, deep research and image generation. On an own server, a chat front end, document search and connections to your systems are set up as part of the project, as our guide to a private ChatGPT alternative lists. Web search, image generation or voice need further components and a decision on what may leave the server.

ChatGPT Enterprise page and business pricing page, read 25 October 2026; gpt-oss-120b model card on Hugging Face, read 25 October 2026. Our summary.

The last row matters in regulated or audited work. A provider can update and retire models on its own schedule, while on your own server the model that answered last month answers the same way until you replace it after a test.

What drives the cost on each side

We publish no prices, because subscription rates and hardware prices both change. The drivers can be compared without them.

Subscription. Seats times months, with the seat type on Business and usage credits on Enterprise. The cost rises with every employee who gets access, whether that person asks one question a week or fifty a day.

Own server. A one-off purchase sized by card count, plus electricity, the room or rack, the setup and support service, any software licences such as NVIDIA AI Enterprise, and the time of whoever owns the system internally. The server costs the same whether it is busy or idle, so it pays off with steady use, and it does not grow when another 50 staff get access, as long as the conversations at the same moment still fit.

Our summary of the cost drivers; seat model from OpenAI's business pricing page, read 25 October 2026.

Electricity is the running cost people ask about first: a two-card server draws about 1.8 kW at full load by our estimate, and our guide to AI server electricity per year turns that into kilowatt-hours. For the full comparison of buying and renting GPUs, see our guide to cloud GPU rental or own GPU server.

Which server for how many staff

A server is sized by the conversations that run at the same moment, which is a small share of the staff. Our users-per-card rule takes 90 per cent of the 95.6 GiB that the driver reports on a 96 GB RTX PRO 6000, less 3 GiB per card and less the model. gpt-oss-120b takes 60.8 GiB, which leaves room for 19 conversations of up to 32,000 tokens on one card (a token is a word or part of one), or 4 at 128,000 tokens.

Example values of our company-size guide (40 per cent of staff in the busiest hour, six requests an hour each, 30 seconds per request, peak factor 2); conversations at 32K by our users-per-card rule for gpt-oss-120b with a 16-bit cache, four cards as two copies split across pairs; power by our estimate (cards at 600 W, processors, plus 15 per cent for the rest of the server).

The entry server on our AI servers page has two RTX PRO 6000 Blackwell Server Edition cards, two AMD EPYC processors, 384 GB of DDR5 ECC memory and two NVMe drives; one card runs the language model and the second is split into isolated parts for the search models. Two servers keep the assistant running when one fails or is updated. All of these are rack servers for a server room or a rented rack, and the four-card server needs 32 A or three-phase power. Our company-size guide explains how the peak is worked out, and a pilot replaces our example values with your own.

When a mix of both fits

Many companies end up with both. Confidential documents, customer data and HR questions go to the assistant on the own server; tasks with public information, or those that need the newest model, use a cloud service on the terms your data protection officer has approved. On the platforms we build, public APIs such as OpenAI's are used only when you enable them for a specific task, and what goes there is visible in the query log. Our guide to hybrid AI, confidential data local and the rest in the cloud shows how to draw the line, and our article on trade secrets and AI tools covers customer drawings and recipes.

What we supply

You can buy this system from us: our team sizes the configuration to your workload and budget and replies within one business day. We build the servers to order with two, four or eight RTX PRO 6000 Server Edition cards, assembled and tested before delivery, delivered anywhere in the EU on one EU contract and invoice, with manufacturer warranty on every component handled through us; quantities and lead time are confirmed in writing with the quote. Our Private AI/ML service, delivered by our engineering partner Vixen.UNO, sets up the model, the chat front end, the search on your documents and the query log, and starts with a pilot on one process. Describe the tasks you would move from the subscription, and we size the server for them.

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