Committing a company's architecture to a single language model means assuming a dependency on something whose price, capacity, and terms change several times a year. It's not a matter of distrusting any vendor; it's that the pace of market change means any such decision becomes obsolete before it pays for itself.
The correct way to treat a model is as a replaceable component, just like you would treat a database or a transactional email provider.
The discussion about full autonomy is often framed as a question of technological maturity. It is more useful to frame it as a question of economics: where is the expensive labor and where is the expensive risk?.
Stanford HAI documented that the inference cost for a system with capabilities equivalent to GPT-3.5 decreased by more than 280 times between November 2022 and October 2024, falling from approximately $20 to $0.07 per million tokens. This magnitude of decline is unprecedented in technological infrastructure.
There are three practical consequences:
What is expensive today may be cheap in a year. Use cases previously discarded due to cost become viable again without any intervention.
What is the best model today may not be in six months. Leadership has changed hands several times.
The models are being retired. Versions have a life cycle, and those who built on a specific version end up migrating out of necessity, not convenience.
An architecture that does not allow taking advantage of the first consequence or absorbing the third is permanently overpaying.
It doesn't mean using multiple models at once or building a complex abstraction layer. It means that Changing the model of a specific step is a configuration and not a project. Four conditions:
Element | Where should he live? | Consequence if you live in the provider's area |
|---|---|---|
Flow logic | In your orchestration | Every model change is a redesign |
Prompts and instructions | Versioned in your repository | They cannot be compared or audited |
Assessment set | In your system | It's impossible to know if the change is for the better or for the worse. |
Record of executions | In your infrastructure | You lose your historical data when you change. |
The third row is crucial. Without a specific set of case studies with the correct answer for your business, the question "Would this other model be better for us?" has no objective answer. And those who can't compare can't negotiate or decide: they can only trust.
Public model performance tables are for guidance only, not for making decisions. They measure general capabilities based on standardized tests; your case is specific and depends on your terminology, documentation, error tolerance, and budget per case.
It's common for a model that ranks lower in general comparisons to perform better in a specific case, especially if it's cheaper and the case doesn't require complex reasoning. This can only be discovered by testing with your own specific cases.
That's why the assessment set is the most enduring asset of an AI project: it outlasts models, allows for change without blind risk, and transforms a decision based on faith into a data-driven one. We developed it in AI doesn't eliminate QA, it makes it more important
It would be dishonest to present independence as free. It has a cost, and there are legitimate reasons for concentration:
The difference between concentrating by choice and concentrating by accident is that in the first case, the ability to opt out is preserved: the logic, prompts, evaluation, and record-keeping remain under your control. Concentrating consumption is a reversible business decision; externalizing the logic is not.
Three steps, none of which require rebuilding the system:
With these three steps, the next time prices change or a better alternative appears, the decision will be commercial, not technical. Which is exactly where it should be.
This makes recording the validation a requirement, not a best practice. And it has a design implication: monitoring that occurs but isn't recorded proves nothing.
Before the next renewal with a model supplier, it's advisable to answer this: How long would it take us to evaluate an alternative, and how long would it take to switch if it turned out to be better?
If the answer is "days to evaluate, weeks to change," the position is sound. If the answer is that the flows would have to be redone, the company isn't choosing a supplier: it's inheriting one every year.
Because prices, capabilities, and availability change several times a year. Stanford HAI documented that the inference cost for a GPT-3.5 equivalent system fell by more than 280 times between late 2022 and late 2024, and specific versions are retired over time.
Replacing the model of a specific step should be a configuration, not a project. This requires that the flow logic, versioned prompts, evaluation set, and execution log reside on the company's own infrastructure, not the vendor's.
They serve as a guide, not a decision-maker. They measure general capabilities based on standardized tests, while each case depends on its own terminology, specific documents, tolerance for error, and budget. A lower-ranked model may perform better in a specific case.
It's a collection of real-world case studies with the answers the organization considers correct. It survives changes in business model, allows for objective comparison of alternatives, and transforms the decision to switch providers into a data-driven one rather than an act of trust.
When there are relevant volume discounts, data residency requirements that only that provider meets, native integration with the platform already in use, or a small team for whom maintaining abstractions would be disproportionate, the key is to retain the ability to exit.
With three steps that do not require redoing it: extract the prompts and instructions to your own repository and version them, build an evaluation set with fifty or one hundred real cases, and isolate the model call behind your own fine interface.
How long would it take you to switch models if the price went up tomorrow? We built the abstraction layer, the evaluation set, and the record so that the decision is back in your hands. Let's talk → |
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