Llama (Meta)
Llama is a family of open-weights language models from the company Meta, published in several versions (Llama 2, Llama 3, Llama 4). Unlike models available solely through an API, Llama models can be self-hosted, adapted and fine-tuned. They therefore offer companies particular advantages in data protection, control and independence.
Also known as: Meta Llama, LLaMA, Llama 2, Llama 3, Llama 4
What is Llama?
Llama is the name of a series of large language models developed by the technology company Meta and released as open models. Several generations have appeared over time, among them Llama 2, Llama 3 and Llama 4, each bringing improvements in language understanding and capability.
The decisive difference from many other models is their open character: the model weights are made accessible, so companies and developers can use the models not only through someone else’s Interface use, but run themselves and adapt to their needs.
What does open weights mean?
The term open weights describes models whose trained parameters (the model weights) are publicly available. Such a model can therefore be run on your own infrastructure without data necessarily having to be transferred to an external provider. That distinguishes Llama from many proprietary models offered solely through an API.
It is important that open weights is not necessarily the same as fully open-source software. Use is generally subject to certain licence terms. The availability of the weights still opens considerably more room to manoeuvre than with purely closed models.
What are the benefits of Llama for companies?
A central advantage of open models such as Llama is the option to self-host. Sensitive data can thus be processed within your own infrastructure, which matters particularly for privacy-critical applications and regulated industries. Companies keep full control of their data.
Added to this is the option of Fine-tuning deliberately to specific tasks, subject domains or your own style of language. Independence from a single provider and potentially better cost control at high usage are also among the advantages that make open models attractive.
When does using Llama make sense?
Llama suits scenarios in which data protection, data sovereignty and control over processing come first. If data may not leave the company or strict compliance requirements apply, a self-hosted open model is often the better choice over purely cloud-based services.
Open models are also interesting for companies wanting to adapt a model deeply to their own requirements or seeking long-term independence from individual providers. Running them yourself does require the corresponding technical know-how and suitable infrastructure, which has to factor into the decision.
What should you watch out for in use?
Running open models brings responsibility: companies have to provide infrastructure, keep models current and see to security and performance. Unlike a managed service, the operational effort lies in-house or with a contracted partner.
The licence terms in question are also to be checked carefully so the models are used on a sound legal footing. A well-founded weighing of open models against API-based services helps find the optimal solution for the use case at hand; a hybrid approach is often advisable too. Elisabit advises on this choice and the integration.
Frequently asked questions
What is Meta's Llama?
Llama is a family of open language models from Meta, released in several versions (Llama 2, 3 and 4). As open-weights models they can be self-hosted and adapted to your own requirements.
What does open weights mean for Llama?
Open weights means the trained model parameters are publicly available. Companies can therefore run Llama on their own infrastructure without necessarily transferring data to an external provider. Use is subject to certain licence terms, though.
What advantages does Llama offer over API models?
Llama can be self-hosted, so companies keep full control of their data, an important advantage for data protection and compliance. The models can also be adapted by fine-tuning and offer independence from any single provider.
Which use cases is Llama suited to?
Llama is particularly suitable when data protection, data sovereignty and control come first, for instance in regulated industries. It is also a good choice for deep adaptation to your own tasks or a wish for independence from individual providers.
What does running Llama require?
Running Llama yourself requires suitable infrastructure and technical know-how for deployment, maintenance and security. The licence terms should also be checked. A hybrid approach of open and API-based models is often sensible.
Related terms
An LLM is an AI language model that understands and produces text by predicting the most likely next word.
Fine-tuning is the targeted retraining of a pre-trained AI model for a specific use case.
A foundation model is a broadly pre-trained AI base model that can be adapted to many tasks.
Generative AI independently creates new content — text, images, audio or code — based on patterns it has learned.
Protecting AI systems and their data against risks such as prompt injection and data leaks.
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