ChatGPT vs Claude for Business: Which to Use, and Where Your Data Goes

There is no single winner in ChatGPT vs Claude, and an article that names one is out of date within months. What lasts is a better way to choose. Treat it as two separate decisions: which assistant your staff use at their desks, and which model sits behind an automation you build. The answers can differ, and the data terms differ between plans, so the plan matters as much as the brand.

This guide is for a UK owner or operations lead. It covers what the business plans and APIs say about training, retention and data location, as we read them in November 2026. It then covers the cloud routes to the same models, when a privately hosted open model makes sense, and how to compare models on your own files. We help clients with this choice through AI strategy consulting, and part of that work is comparing OpenAI, Anthropic and open models against your data, budget and privacy rules.

Key takeaways

• Decide the staff assistant and the model behind an automation separately. They have different risks, different buyers and often different answers.
• Check the data terms of the exact plan or API you will use. Anthropic says its commercial products are not used for training by default, and OpenAI says the same of API data, but retention and data location differ.
• UK data location is thin today. OpenAI offers UK storage at rest to eligible Enterprise, Edu and API customers, with conditions, while Anthropic’s page lists only US-only or global processing.
• Compare models by running your own documents through both, marked by the people who do the work, not by reading benchmarks.
• Both tools get things confidently wrong. A “never paste” rule and a short AI use policy matter more than which logo you pick.

Which should staff use: ChatGPT, Claude or Copilot?

Staff should use whichever assistant your business can put under a business plan with admin control, and the choice between brands comes second. The three assistants most owners ask about are ChatGPT, Claude and Microsoft 365 Copilot. They differ less in raw ability than in how they connect to your files and who controls the account.

Assistant	Works inside	Check first
ChatGPT	OpenAI’s own app	Plan terms, admin
Claude	Anthropic’s own app	Plan terms, admin
Microsoft 365 Copilot	Your Microsoft tenant	File permissions

Microsoft 365 Copilot is the odd one out because it works inside your Microsoft tenant. Microsoft’s privacy page says Copilot only surfaces organisational data to which individual users have at least view permissions. It also says prompts, responses and data accessed through Microsoft Graph are not used to train foundation models. If your files already sit in SharePoint with sensible permissions, that is a real advantage. If they do not, Copilot will expose the mess, because it can only respect permissions that exist.

For ChatGPT and Claude, the practical points are the same. Put everyone on a business plan under a company account, not personal logins, so you can remove a leaver and see who has access. Keep personal and free accounts away from client work. Our AI literacy workshops train teams on the assistants they will actually use, including what each is good at and where both confidently get things wrong.

Do ChatGPT and Claude train on your business data?

Anthropic says its commercial products are not used for training by default, and OpenAI says the same of API data. Plan coverage and wording differ, so read the page for the plan you will buy.

On the Anthropic side, its privacy centre says that by default it will not use inputs or outputs from its commercial products for training. It adds an exception: if you report feedback or bugs, or otherwise choose to allow it, Anthropic may use those chats to train its models. The commercial terms go further and say Anthropic may not train models on Customer Content from the Services, and that the customer owns its outputs. Anthropic keeps consumer plans (Free, Pro and Max) on a separate page, so do not assume the business answer covers them.

On the OpenAI side, its business data page says that by default it does not use data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu or the API platform, inputs or outputs included, for training or improving its models. The API documentation says the same of API data unless you explicitly opt in. The business page also lists admin features for ChatGPT Business, including roles, SAML single sign-on and controls over third-party GPTs. Personal ChatGPT accounts sit outside these commitments, so read the terms for the plan you intend to buy.

Two habits cover most of the risk. First, buy the business tier, never let client data go into a personal account. Second, write down the date you checked the terms, because vendors revise them. Regulation sits with you: under UK GDPR your business remains responsible for what staff send. The ICO’s guidance on AI and data protection covers accountability, transparency, lawfulness and fairness, and it notes it is under review. This is general information, not legal advice.

How long do they keep your data, and where is it processed?

Retention and location are where the two vendors differ most, and both change with the route you take. For the APIs, the pages we opened say the following.

Question	OpenAI API	Anthropic API
Default log retention	Up to 30 days	Deleted within 30 days
Zero retention	Needs approval	Via agreement
UK or EU option	UK storage, EU region	US-only or global

Behind those cells, OpenAI says abuse-monitoring logs are retained for up to 30 days by default. Its zero data retention option needs prior approval. It lists a United Kingdom region (storage) and a Europe region (storage and processing). Any non-US region needs approval for abuse-monitoring controls and a Modified Retention amendment, so it is a contract step and not a switch.

Anthropic says it automatically deletes API inputs and outputs from its backend within 30 days of receipt or generation, unless an exception applies. Zero data retention exists by agreement. Its data residency page lists two inference settings today, global or US only, and says the US is currently the only workspace geo for stored data. It does not list a UK or EU option.

For the staff assistant, OpenAI’s business data page says eligible ChatGPT Enterprise and Edu customers, along with API customers, can store content at rest in the UK, among other regions. ChatGPT Business is not named in that list.

If UK or EU processing is a hard requirement, that difference may decide the question for the API route. Check again at purchase, because both pages are moving targets.

Which model should sit behind an automation?

The model behind a workflow is a per-project decision, and the honest first step is to test two or three candidates on your own documents. A staff assistant is judged by people. A model inside an automation is judged by whether the output is right, repeatable and cheap enough at volume.

We use more than one provider because the right answer depends on the job. We have shipped OpenAI models in client products, for reading and marking free-text answers. ChatQuote, our own AI quoting product, runs on Anthropic’s Claude models. In the sales automation we built for SBU, an energy business, Claude writes the outreach emails, classifies replies and drafts responses, with an 85% confidence threshold deciding what runs automatically and what waits for a person.

That is one provider per project, chosen for that project. It is not a claim that either is better. In our process automation work, these models handle the steps that need interpretation, such as reading an email or summarising a form, and a person reviews the results where the risk is higher. If a step follows fixed rules, plain code or a workflow tool is cheaper than any model.

Can you reach the same models through Azure or AWS?

Yes, for some models, and it can help if your business already buys cloud services from Microsoft or Amazon. The data terms then come from the cloud provider’s own pages, not the model maker’s.

Microsoft says prompts and completions on its Azure models are not available to OpenAI or other model providers and are not used to train foundation models without your permission. It also says processing stays in your chosen geography unless you pick a Global or DataZone deployment type. AWS’s Bedrock FAQ says users’ inputs and model outputs are not shared with any model providers.

There is a catch with Microsoft 365 Copilot. Its privacy page says models provided by Anthropic as a subprocessor are currently excluded from the EU Data Boundary. If you switch on a Claude-powered feature inside Copilot, ask Microsoft where that processing happens first.

Which route is right depends on how you already buy software and who signs the contract. A route through your existing cloud agreement can be simpler than adding a new vendor to your supplier list, and our AI consulting work usually starts from what you already run.

When should you host a private open-source model?

Host a private model when the data cannot leave your own cloud, or when no hosted vendor’s terms fit. Open models are published on places like Hugging Face, whose documentation says its Hub hosts over 2M models. You download one, run it on infrastructure you control, and prompts never go to a third party.

The trade-offs are real. You carry the hosting cost, the updates and the monitoring. Licences differ between models, so check each one before commercial use. Open models are often less capable than the best hosted ones on hard reasoning, though for narrow jobs such as classifying emails they can be enough. Test that on your own files before you plan around it.

We offer this as part of AI workflow work, with an open model we host for you where the data cannot leave. It is a service we offer rather than a shipped case, so we will say so if you ask for a reference. For many UK firms a hosted business plan with sensible controls is enough, and a private model is the right answer for a small number of them.

How do you compare models on your own documents?

Run the same real tasks through each candidate, hide which model wrote which answer, and let the people who do the work mark them. Public benchmarks measure general ability, not your contracts, your tone or your messy spreadsheets.

A simple method, which is our suggestion and not a standard:

1. Collect 30 to 50 real tasks from the last month, with the documents attached.
2. Write down what a good answer looks like before you run anything.
3. Run each task through each candidate with the same instructions.
4. Have two people mark every answer blind, and record cost and time too.
5. Re-run the set when a vendor releases a new model.

Keep the set, because it becomes the test you reuse. If you later add search over your own files, the same questions test that too, as we explain in what RAG is.

Where both get things wrong, and what to write down

Both tools produce confident answers that are wrong, and no plan upgrade removes that. Ask for sources, check numbers and names yourself, and keep a person responsible for anything that goes to a client.

The rule we teach first is what never gets pasted into a public chatbot: client data, contracts, anything you would not email a stranger. Put it in a one-page AI use policy covering approved tools, banned data, who checks outputs and who to ask. A workshop is a quick way to start one, and our AI readiness checklist covers the wider groundwork.

One more point for teams building connectors. OpenAI’s documentation describes connecting your own Model Context Protocol (MCP) server to ChatGPT, and the same server can serve Claude too. Our guide to what an MCP server is explains the protocol.

A sensible next step

Pick one real task, run it through two tools, and write down which plan and data terms you would need. Then decide who owns the policy. If you want an outside view across your data, budget and privacy rules, our AI audit runs from £1,500 to £6,000 over two to three weeks, and a half-day team workshop from AI literacy workshops starts from £1,500. For a quick check of where AI is worth using at all, see our AI readiness assessment.