AI is increasingly weaving its way into our work and daily lives, and it will only continue to do so more and more.
The use of AI is becoming increasingly common, whether in our everyday questions or in our work. Most of the tools currently used are nevertheless hosted on third-party servers, somewhere far away and outside our control. Faced with these problems and many others caused by third-party hosting, self-hosting open-source AI models can be a plausible solution. It is a proposition worth studying; its seriousness can be attested to, for example, by the Federal Council, which is about to implement its own internal AI (self-hosted, therefore) based on an open-source model (Llama 3) to help with the tasks of the federal administration.
Localized AI hosting consists of running the models on one’s own physical machines. In this case, the machine on which all the calculations are performed is physical, in your possession, and on your network.
The Advantages
What are the advantages of self-hosting an AI compared to cloud services offered by third-party companies?
First of all, the fact that AI systems are physically located close to the final place of use (you), or even on the same network, means that the signal of your requests and responses has “less distance” to travel. Latency is thereby reduced, which creates AI systems that are faster to use.
Next, with a server that belongs to you, the data you send to the AI—whether requests, prompts, text files, videos, images, etc.—that you give it remains under your control. This is particularly interesting if you need to process sensitive or even confidential data. This advantage should also be taken into account for the simple reason of respecting privacy and confidentiality. Even if you or your company do not handle sensitive data, using services hosted on servers in countries with more lax laws on the handling of user data can lead to privacy violations. Added to this is the fact that AI services are able to use the data provided to them to train their models, and furthermore, by entrusting one’s data to third-party operators, one is exposed to the risk that these third parties get hacked or that their data leaks. By setting up an AI system on your own infrastructure, you guarantee that your data does not leave, or at least stays on, your machines, which allows you to keep complete control over it. Moreover, in cases where you pass client data through the AI or use it to process files that include client data, you have the means to keep control over it without sending it to third-party companies, while guaranteeing compliance with European legislation on the matter. It is nonetheless important to note that various AI services include, for some of them, security guarantees. The paid version of Microsoft Copilot, which has access to the files on your computer, guarantees that all calculations and stored data are on Microsoft Azure servers in Europe.
Customization is another asset of a self-hosted AI. With third-party services, the models offered are rigid and “defined”. There is not really room to tailor an AI to one’s liking. Self-hosted models first allow you to adjust the parameters of the various models more precisely, in order to make responses more relevant and accurate, but also so that the responses given are more appropriate, especially for your business domain. This also allows models to be trained on your own dataset, in order to have even more precise responses in specific domains. Self-hosting the AI makes it possible to give the artificial intelligence access to your company data stored on your servers, in the same way that the paid version of Microsoft Copilot has access to your data on your computer. The advantages are the same: the AI’s responses become “personalized”; it is able to answer specific questions “internal” to the company whose answers would be in the data, files, documents, reports… This greatly simplifies operations, and from that point of view, the AI can truly serve as a personal assistant. The paid version of Microsoft has a similar function for data stored on their Azure servers.
The drawbacks and constraints
The first constraint for implementing this solution is the infrastructure. AIs need a large amount of computing power; this computing power is provided by graphics processors (GPUs). The GPU market is practically monopolized by a single company—nvidia—and they are the sole manufacturers of the most powerful graphics processors. Solutions do exist, however, that try to run them exclusively on central processors (CPUs), for example LocalAI. GPUs cost more or less depending on the power needed to run your models, because yes, each AI model has a different size, and even a single model can be offered in several sizes. A larger model—size being calculated by the number of parameters—allows for more precise and faster responses, as well as processing a greater quantity of data. Current prices nevertheless remain affordable, especially for small and medium-sized configurations; one can count on an initial estimate of 2,000 to 5,000 CHF. The price can then climb effectively toward infinity. All this infrastructure of course consumes electricity, which can generate a certain cost.
The second drawback is that, as with all self-hosted solutions, deploying it requires technical knowledge of the subject. Indeed, for someone who is not familiar with servers, connectivity, etc., this type of task can prove arduous, but for that we have a solution and we can advise and help you on the subject.
As a result, using a cloud AI service can prove practical, as it is simpler to use and requires a lower initial investment. For tasks that would require rare use of AI, the added value of a self-hosted AI would be quite minimal, or even nonexistent. It is therefore necessary to carry out a cost analysis and define the goals and scope of using this type of system in your company.
Example of an open-source tool for self-hosting: LocalAI
LocalAI positions itself as a free and open-source replacement for OpenAI. Its main goal is to offer a self-hosted alternative to OpenAI’s APIs, in such a way that replacing an OpenAI API with LocalAI’s in your projects is simple and effective. LocalAI makes it possible to run AI tools, such as LLMs, image generators, and text-to-speech tools, directly on your own server. LocalAI also prides itself on being able to run using only CPUs. This means there is no need for overly powerful infrastructure.
The models supported by LocalAI are numerous; the full list can be found at: https://localai.io/gallery.html . It notably includes Gemini 3 from Google, Deepseek r1, and Llama 4 Scout from Meta (formerly Facebook). The three models cited are quite powerful, notably Llama and Deepseek, which rival the leaders in the field, whether ChatGPT (Deepseek is moreover more performant than ChatGPT on many points) or Gemini from Google.
Conclusion
In conclusion, self-hosting AI is an interesting solution and presents several advantages, although its implementation comes with some difficulties and constraints. With this system, latency is reduced, and data is guaranteed to remain in a closed environment. Moreover, models can be customized, tailored to your needs. All these points are particularly interesting for companies that operate in specific contexts, those swimming in sensitive files or in industries with a great need for personalized automation. Tools exist, such as LocalAI, to implement this alternative. However, technical expertise is necessary to set up these solutions, which can entail additional costs, and the infrastructure requires a more or less substantial initial investment. In short, AI is a technology that is still in its early days. Self-hosting it allows access not only to a new automation solution, but also to a capacity to experiment with these new techniques and see their usefulness in your own area of expertise.
