Local AI or ChatGPT Enterprise: an honest comparison for a Quebec SME

The “ChatGPT at C$27 against a $50,000 project” comparison is rigged, because it compares a subscription with an asset. The subscription rents you a brilliant model that knows nothing about your business: every employee explains the context again in every conversation. The local enterprise brain knows your quotes, your procedures and your history, answers by citing your documents, and works without anything leaving your walls.

Both have their place, and this article ends with the situations where the subscription is objectively the right choice. But to compare, you have to put the three real dimensions on the table: total cost, the path your data takes, and knowledge of your business. Let us start with prices, checked on the official pages in July 2026.

Here is what the business offerings actually cost in July 2026. Prices change; each one was taken from the publisher’s official page on the date of this article.

Serious publishers state it clearly: OpenAI writes “No training on your business data by default” for Business and Enterprise, and Anthropic “No model training on your content by default.” That is true, contractual, and real progress. But training was never the only issue.

Your queries and your documents pass through and sit on the servers of American companies. The CLOUD Act lets US authorities require an American-owned provider to hand over data, even when it is hosted in Canada: the server’s location is not enough, what counts is the owner’s jurisdiction. For a firm bound by professional secrecy, a manufacturer whose quotes are worth millions, or any business handling personal information, the Law 25 question therefore stands in full: disclosure outside Quebec, a prior privacy impact assessment, contractual clauses to negotiate.

Against that, local AI settles the question by construction: the documents do not leave your infrastructure, the assessment gets simpler by the same measure, and no change of terms of service can land on you from the other end of the contract. This is not a moral argument, it is an architectural one.

A generic subscription writes, summarizes and reasons remarkably well. But ask it the question that matters (“what is our typical margin on this kind of structure?”, “how do we handle this case under our procedures?”) and it does not know, and it sometimes answers anyway. The business offerings add connectors to your documents, at the price of sending those documents into their cloud, which brings us back to the previous section.

The local enterprise brain is built the other way round: it starts from your documents (the RAG approach, explained in our glossary for executives), answers by citing the source and the page, respects your access rights file by file and logs every consultation. That is what turns a gadget into a working tool, and it is precisely the part no subscription delivers out of the box.

There is also the inverse economy of scale: the cost of a subscription grows with every seat; the cost of a local server does not move whether ten or a hundred people query it. Our pricing grid sets out the investment ($45,000 to $85,000 for a complete enterprise brain, before support, plus the server), and the funding packages reduce the net in a way no subscription benefits from: the programs fund transformation projects, not monthly fees.

Neither path is universally better. Here is the decision grid we use at scoping, including when it concludes against us.

Three points of honesty to finish. One: the major publishers’ frontier models remain better than open models at the finest reasoning; if your use lives on that frontier and your data allows it, the subscription or the API can be defended. Two: local AI carries a responsibility the subscription spares you, that of an asset to maintain (updates, backups, roughly 10% to 20% of the implementation cost per year). Three: the subscription prices quoted here are those of July 2026; they change at the publishers’ discretion, which is itself one of the arguments for local, where the cost is yours.

Full transparency: Cogio designs and deploys local enterprise brains, that is our trade. This comparison reflects how we read the field, with official prices to back it up; challenge it, in particular with the 12 questions to put to any firm.

Do the exercise in three columns: your use cases, the sensitivity of the data in each, and how many users are involved. If everything lands in the first row of our grid, take the subscription and a usage policy, and come back when the document-heavy need grows. If even one row touches sensitive data or company knowledge, cost out the local route before signing anything: our Funding page lists the programs, and the scoping call puts a net figure on your scenario.

And if you are torn between the two, the order of operations matters: it is easy to add a subscription alongside an enterprise brain; it is expensive to bring habits and data back from the cloud.