LLaMA and Open Models

In 2023, Meta released its LLaMA family of models and, shortly after, Llama 2 with a license that allowed it to be used even for commercial purposes. It was a quiet turning point: for the first time, a high-capacity language model became available to download, study, and run outside a single provider’s servers. The conversation about artificial intelligence, which until then revolved almost entirely around cloud services accessed only over the internet, began to change.

For a Mexican small business, the important news isn’t technical but strategic. Open models open the door to something that once seemed reserved for the big tech companies: using AI without handing your information to a third party and without being tied to a single platform. In other words, AI you can run on your own infrastructure, on your terms. Let’s look at what that means in practice and when it makes sense.

What an “open model” is (and isn’t)

An open model is one whose weights—the already-trained “brain”—can be downloaded and run on your own machine or server, instead of being accessed only through the cloud of whoever created it. Not all of them are the same, and not all are entirely free: each comes with its own license and terms. But the underlying idea is powerful:

• You decide where it lives. It can run on your own server, in a data center in Mexico, or in the cloud you choose, without depending on a single owner.
• Your data stays with you. The sensitive information the AI processes doesn’t have to leave your house; you keep your data, processes, and history.
• No cost per query. Instead of paying for every call to an external service, the main cost is the infrastructure you already manage.
• Less dependence on a provider. If prices or terms change tomorrow, you’re not left without an operation.

This doesn’t mean an open model is always the best option. Sometimes a cloud service is simpler and cheaper to get started. The beauty is in having the possibility to choose based on the case.

Why it benefits a small business

The most underrated advantage of open models is control. Many companies handle information they shouldn’t send to a public service: customer records, payroll data, medical histories, contracts. Being able to process all of that with AI without it leaving your environment completely changes the conversation with your legal team and with your customers.

The question is no longer “can I use AI?” but “where do I want my data to live while I use it?”

Add to that predictable costs and customization. A model running on your infrastructure can be adapted to your vocabulary, your documents, and your way of working, instead of forcing you to conform to a generic tool. It’s the same logic as custom software: instead of renting a one-size-fits-all solution, you build something that fits your operation and grows with you.

Cases where it already adds value

You don’t need a giant project to take advantage of them. Some concrete, focused uses:

• Smart search across your documents. Ask in natural language about manuals, policies, or internal contracts, without uploading them anywhere.
• Classification and summarization. Sort emails, tickets, or requests and summarize long texts so your team can decide faster.
• Internal assistants. Support that answers frequent staff questions using your own documentation as the source.
• Automation of repetitive tasks. Draft copy, extract information from forms, or prepare reports, always with human review.

In every case, the goal is the same: free up hours of repetitive work and make decisions with reliable information, without losing control of what’s yours.

Take the first step with your own AI

The key is not trying to solve everything at once. Start with something small and with clear goals:

• Choose a single real problem. One that consumes time today and is easy to measure (for example, answering internal questions or summarizing requests).
• Organize your data first. Gather the documentation the AI will use; a good result depends more on good information than on the biggest model.
• Test small. A pilot of a few weeks, with clear metrics, says more than any promise.
• Decide with a cool head where it runs. Not everything has to be your own model; sometimes the cloud is the way to start and migrate later.
• Team up with someone who translates. A technical partner who understands your business keeps the project from becoming an aimless experiment.

Deciding where your information lives doesn’t have to be a topic reserved for the big tech companies. At Normandia Web we help you identify where an open model adds real value, set up a test pilot, and build the solution tailored to your operation, with your data always under your control. If you’re up for exploring it, let’s talk and design that first step together.