Claude or ChatGPT: which should your company choose?

When people say Claude or ChatGPT, they mean two different things, and the confusion skews many comparisons.

The first is the app: a chat window, in a browser or on a phone, where each person asks their questions. It is paid per user, per month. Your staff paste in text, upload files and get an answer back. Nothing is connected to your tools.

The second is the API: the same model, called by your own software. This is what lets you summarize a call report automatically in your CRM, draft a reply in your inbox or query your document base. It is paid by usage, based on the volume of text sent and received.

The choice is not the same in the two cases. For the app, you are choosing a working tool for people: habit and ease of use matter. For the API, you are choosing a technical component: quality on your tasks, cost at your volume and the contract's commitments matter, and the name on the label much less.

Both can write, summarize, translate, analyze a table and write code. The gaps narrow with every new version, and a ranking published today ages fast. Here is what most often sets them apart.

ChatGPT is the most widely used assistant. Your staff have often used it already, which shortens onboarding. It is comfortable with short, varied tasks: rewording an email, translating a paragraph, suggesting an outline, sorting a list.

Claude holds up over a whole document and keeps to a tone instruction across an exchange. On a forty-page contract, a requirements document or a codebase, those two qualities save time.

None of these observations replaces a trial. The only comparison worth having is run on your documents, with your questions, scoring the answers without knowing which model wrote them.

Everything you type into an online assistant passes through its vendor's servers. The useful question is what the vendor is allowed to do with it.

On business plans and on the APIs, both vendors commit not to train their models on your data. On consumer plans, the rule depends on the account settings. A staff member who handles a client file with a personal account is therefore outside the framework you think you have. That is the first risk, and it is settled by a clear internal rule, before any choice of tool.

Both vendors are American: even with hosting in Europe, they can be required to hand data over to US authorities as part of legal proceedings. For most uses, that is not an obstacle. For data covered by professional secrecy, health data or sensitive files, it is one, and neither is suitable as is.

In that case, the answer is a model that runs in France or on your own infrastructure. Our AI projects follow that rule: data is hosted in France by default, or on your premises when its sensitivity requires it.

Prices change often and we do not reproduce them here: each vendor publishes them. The mechanics are stable, and they are what you need to understand.

The app is paid per user, per month. The budget is easy to forecast: the number of people equipped, times the price of the plan. The trap is to equip everyone on principle. In most teams, some accounts are used every day and others almost never.

The API is paid by usage. Each request costs according to the amount of text sent and received, and according to the model called: the most powerful cost far more than the fastest. The same job can therefore see its bill vary widely depending on the model chosen and the length of the documents.

That is why you do not choose one model for the whole company. You choose, task by task, the smallest model that gives a correct result: a fast model to sort emails, a powerful one to analyze a contract. That rule weighs more on the bill than the choice between the two vendors.

The two APIs are alike. They take a text and an instruction, and they return a text. Moving from one to the other is not a big job, on one condition: the integration was designed for it.

In practice, your software does not talk to the model directly. It goes through an intermediate layer that belongs to you, holding the instructions, the control rules and the test set. The day another model becomes better or cheaper on your task, you plug it in behind that layer, rerun the test set and compare.

That test set is the most useful asset in the project. A few dozen real cases, with the expected answer, are enough to tell whether a change of model improves or degrades the result. Without it, you choose on an impression.

The instructions, the intermediate layer and the test set stay with you. That is what we mean by owning your intelligence: the model is rented, the know-how around it is yours.

You want to equip a team for general use, with no integration project: take the one your staff already use, on a business plan, with a written rule on what does not go into it. That is often ChatGPT.

Your team writes, reviews long documents or develops software: try Claude first.

You want to connect a model to your tools: do not decide on the name. Have both tested on a test set drawn from your documents, compare quality and cost at your volume, and keep the option to switch.

Your data is sensitive: neither one in its online version. You need a model hosted in France or on your own infrastructure, and a written framework before the first line of code.