{"id":33760,"date":"2026-10-22T22:08:16","date_gmt":"2026-10-22T22:08:16","guid":{"rendered":"https:\/\/thecloud.group\/?p=33760"},"modified":"2026-10-20T22:08:45","modified_gmt":"2026-10-20T22:08:45","slug":"multi-model-architecture-company","status":"publish","type":"post","link":"https:\/\/thecloud.group\/en\/arquitectura-multimodelo-empresa\/","title":{"rendered":"The smart company doesn't commit to a single model"},"content":{"rendered":"
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
<\/div>\n\t\t\t\t\t\t
\n\t\t\t\n\t\n<\/svg>\t\t<\/div>\n\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"logo\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

The smart company doesn't commit to a single model\n\n\n\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

October 22, 2026<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

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.<\/span><\/p>

The correct way to treat a model is as a replaceable component, just like you would treat a database or a transactional email provider.<\/span><\/p>

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?.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Why is change happening so fast?\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

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.<\/span><\/p>

There are three practical consequences:<\/span><\/p>

What is expensive today may be cheap in a year.<\/b> Use cases previously discarded due to cost become viable again without any intervention.<\/span><\/p>

What is the best model today may not be in six months.<\/b> Leadership has changed hands several times.<\/span><\/p>

The models are being retired.<\/b> Versions have a life cycle, and those who built on a specific version end up migrating out of necessity, not convenience.<\/span><\/p>

An architecture that does not allow taking advantage of the first consequence or absorbing the third is permanently overpaying.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

What does it mean to be independent of the model?\n\n\n\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

It doesn't mean using multiple models at once or building a complex abstraction layer. It means that <\/span>Changing the model of a specific step is a configuration and not a project<\/b>. Four conditions:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Element<\/b><\/p><\/th>

Where should he live?<\/b><\/p><\/th>

Consequence if you live in the provider's area<\/b><\/p><\/th><\/tr><\/thead>

Flow logic<\/b><\/p><\/td>

In your orchestration<\/span><\/p><\/td>

Every model change is a redesign<\/span><\/p><\/td><\/tr>

Prompts and instructions<\/b><\/p><\/td>

Versioned in your repository<\/span><\/p><\/td>

They cannot be compared or audited<\/span><\/p><\/td><\/tr>

Assessment set<\/b><\/p><\/td>

In your system<\/span><\/p><\/td>

It's impossible to know if the change is for the better or for the worse.<\/span><\/p><\/td><\/tr>

Record of executions<\/b><\/p><\/td>

In your infrastructure<\/span><\/p><\/td>

You lose your historical data when you change.<\/span><\/p><\/td><\/tr><\/tbody><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

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.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

The error of generic comparison\n\n\n\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

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.<\/span><\/p>

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.<\/span><\/p>

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 <\/span>AI doesn't eliminate QA, it makes it more important<\/span><\/a>\u00a0<\/span><\/i><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

When is it advisable to focus on one supplier?\n\n\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

It would be dishonest to present independence as free. It has a cost, and there are legitimate reasons for concentration:<\/span><\/p>