Llama AI: what is the open source model of Meta

Llama AI is the family of Language Models developed by Meta (the company of Facebook, Instagram and WhatsApp) and released with Downloadable Model Weights. Simply put: it is a generative AI "engine" similar to those behind ChatGPT or Gemini, with a decisive difference — instead of only running on the provider's servers, you can download it and spin it wherever you want, on your servers or the cloud you choose.

This is where the most common confusion comes in, so let's get this straight: Llama is not "open source" in the strict sense. Meta distributes weights (that's why we talk about Open Weights, or open weight), but the license contains Restrictions on use and is not an OSI approved license such as MIT or Apache 2.0. It is more correct, and more honest, to call it an open-weight model. The difference is not picky: it changes what you can and cannot do with it, especially if you build a product there.

In this guide we see what Llama is, how it is used, what real benefits it offers to a company (data control, customization, costs) and when it is best to stay on a proprietary model.

What is Llama and who develops it

Llama (acronym of Large Language Model Meta AI) is the family name of Great Language Models released by Meta from 2023. Like the others LLM, is a model trained on huge amounts of text for predict the next most likely word: from this mechanism arise answers, summaries, translations, code and everything we associate with generative AI.

The peculiarity does not lie in what does, but in how is distributed. The best-known models to the general public — OpenAI ChatGPT, Claude of Anthropic, Gemini by Google — are closed: you use them through the provider's app or API, but you can't see or download the model. Llama, on the other hand, makes available the weights: the numerical parameters that make up the trained model. With those weights you can run it on your hardware, modify it and adapt it.

Meta releases Llama in more sizes (smaller and faster models, larger and more capable models): the idea is that everyone chooses the size that suits their hardware and task. Names and versions often change, so for updated specifications always check the official page of Meta and the related license terms.

"Open weights" or "open source"? The difference that matters

This is the most misunderstood point, and for a company it is also the most legally important.

• Pure Open Source means OSI approved license (e.g. MIT, Apache 2.0): code and/or freely usable, modifiable and redistributed weights without restrictions of use relevant.
• Open Weights (open weight) means that the weights of the model are downloadable, but the license may impose limits: what you can use it for, under what conditions, above what thresholds.

Llama falls into the second category. Its license allows wide use, even commercial, but is not without constraints and it's not OSI-approved. That's why it's incorrect to simply call it "the open source model of Meta", even if you find it written everywhere.

If you want a complete picture of the category, with the licensing differences between the various projects, the guide to Open Source AI Models goes into detail.

How to use Llama: two roads

There is no one way to use Llama, and figure out which one is right for you. First Decision to make. The two paths have very different costs, skills requirements and privacy implications.

The Practice Rule: If Your Leverage Is the control of data and you have (or can have) technical skills, self-hosting makes sense. If instead you want try quickly without managing infrastructure, start from a cloud provider that offers Llama via API and evaluates self-hosting only afterwards.

Why a company should choose Llama

The advantages of Llama are not "makes more beautiful answers". They are Strategic and Operational, and count in precise scenarios.

• Privacy and data at home: with self-hosting the data does not go out to an external provider. It is relevant for those who process sensitive data (healthy, legal, financial) and for GDPR compliance.
• Deep customization: having the weights you can end-tuning on your domain, adapt the model to your language and documents, which is impossible with a closed model.
• Predictable costs on large volumes: Beyond a certain usage threshold, paying the infrastructure once may cost less than the token price of a proprietary API.
• Supplier independence: you are not tied to price, policy or availability changes of a single vendor. The downloaded model is yours to use.
• No tech lock-in: you can move the model between different clouds or bring it on-premises without rewriting everything.

The common thread is one: control. Llama agrees when control (on data, cost, customization) is worth more than the convenience of "plug and play". If you don't need this control, the extra complexity is not justified.

Practical examples

Two realistic scenarios of Italian realities, to understand when Llama is the right choice and when not.

1. Law Firm — confidentiality of documents. One study wants an AI assistant to search for and summarize information in their own acts and contracts, but the documents contain highly classified data that can't get out to an external provider. Solution: Llama in self-hosting, on a studio-controlled server, connected to a type system RAG that interrogates the internal archive. The data remains inside the walls. Here control is worth more than simplicity, and in fact it is the type of requirement typical of the AI for law firms.

2. E-commerce — high volume assistance. An online store receives thousands of questions a day about shipments, sizes, and returns. With a proprietary API, at those volumes, token cost becomes important. The company rates Llama hosted on a cloud, with a fixed hourly cost of the infrastructure: beyond a certain threshold of demands, it agrees. Below that threshold, no — and here's the thing: The choice depends on the numbers, not by ideology.

Either way the question is not "Is Llama the best model?", but "Is my main constraint data or cost control?". If the answer is yes, Llama comes into play. If it is no, a simpler closed model often wins.

When Llama is convenient and when not

Synthetic rule: Llama is a choice of control, not comfort. If your problem is "I want to try AI quickly", start with a ready tool and evaluate Llama only when real constraints on data or costs emerge. To orient yourself among the options, the guide on how to choose an AI model compares the decision criteria.

Mistakes to Avoid

• Just call it "open source." The weights are open, the license has restrictions. Before building a product there, read the official terms of Meta: it's a legal matter, not terminology.
• Underestimating the costs of self-hosting. "The model is free" is true only of the download. GPUs, power, upgrades and maintenance cost, and on low volumes can exceed the price of an API. Make the total bill before leaving.
• Thinking that on-premise = automatically GDPR-compliant. Keeping data in-house helps, but compliance depends on how you configure access, logs, retention and security. The model is only one piece of the puzzle.
• Choosing the wrong size. Larger models are more capable but require expensive hardware; small ones run everywhere but make less on complex tasks. Start with the task, then choose the size, not the other way around.
• Skipping the test before production. Try Llama on your real use case, with your data, before committing budget to infrastructure. The local test costs zero and avoids surprises.

How to apply it in the company

Introducing Llama in a sensible way does not mean "we install the model and see": it means starting from the constraint that pushes you towards an open weight model.

1. Start with the constraint, not the model. Your problem is data privacy, volume cost, or customization? If you can't answer, you probably don't need Llama (yet).
2. Give it a try at no cost. Run a small local version on your real use case, with sample data. Evaluate the quality of responses before spending.
3. Choose the way: self-hosting or provider. Do you have infra skills? Self-hosting. Do you want speed without management? Cloud provider with Llama via API. You can start with the second and migrate after.
4. Verify license and compliance. Read the official terms of Meta for your type of use and, if you process personal data, align infrastructure and processes with the GDPR.
5. Measure and decide. Compare cost, quality and control over the closed model you would use alternatively. If Llama wins on your numbers, scale; otherwise stay on the owner.

This is exactly the type of assessment in which a AI Consulting It makes the difference: whether self-hosting an open-weight model really suits your case, or whether you're adding complexity without a real return. Often the right choice is hybrid —some processes on closed models, others on Llama for sensitive data —and should be designed, not improvised. If you want to build a tailored system, work on dedicated software and process automation It starts right here.

Conclusion

Llama AI is the family of models at Open Weights Meta's strength is not being "better" than ChatGPT or Gemini ever, but giving you control — on data, costs and customization. It is the right choice when your constraints are privacy (data that stays in the company), large volumes or the need to adapt the model in depth. For general and immediate use, however, a closed model often remains simpler and faster to activate. And remember the key clarification: open weights not is pure open source, so the official license terms should always be read. From here you can continue with the Local AI Models and with the comparison on Mistral AI, the other big European name of open models.

AI becomes useful when it enters processes. Giallo Studio helps SMEs and teams build concrete, measurable and sustainable automations —even by assessing whether an open weight model like Llama makes sense for your case, or take a look at the experiments of the The Lab.