How to Cut Your Organization's AI Costs and Keep Your Data In-House: Two Solutions

The debate over the cost of using LLM systems keeps coming back. CEOs and CFOs receive the bills for AI tools and are not always sure whether it is actually worth it (hint: if you know how to leverage the tools, it very much is).

There is no shortage of advice online on how to save. This time I want to suggest an approach that solves two problems at once: cost, and data security.

One of the requirements I hear most often is that data must not leave the organization's servers. At the same time, everyone wants to use AI to make work more efficient.

The solution many people overlook is to run the models yourselves. It is not a complete solution, but it is significantly cheaper, especially when it is built properly.

Solution one: a GPU server for the organization

A powerful GPU server costing roughly 500 to 2,000 dollars a month, depending on your needs, can serve an organization of 100 employees. In practice, not everyone uses the model at the same moment, and not everyone runs the most demanding queries.

You install open models such as Qwen or Gemma, open an account for each employee, and you can also build a dashboard showing how much the tools are actually being used across the organization.

Solution two: one computer with a good graphics card

I implemented this recently with one of my clients, and it will probably surprise you.

The client needs to produce a lot of video, and anyone generating AI video around the clock knows how expensive that gets. We built a desktop machine with a high-end graphics card, for a one-time investment of around 10,000 shekels (it actually cost a lot less, but you don't give away every secret on the first day).

It works at a Plug & Play level: you feed in prompts and the machine generates. Production is slower than online services such as Kling or Seedance, but economically it pays off handsomely, because the cost per video is close to zero. Even if you invest in a more expensive machine with several cards in parallel, the numbers still work, especially for media and production companies.

Visually, Seedance produces higher-quality output. But in social media performance, the engagement we got was the same.

And the same machine can of course also run a full LLM environment that sits inside the organization, secured and with external access restricted.

In short, the savings are right in front of you. A computer with two or three powerful graphics cards can give a few dozen employees a complete AI solution, inside the company portal and tailored to your needs, for a one-time cost that can serve you for a very long time.

What still belongs with the frontier models

For complex development, local models are not there yet. For serious coding and agent work I keep using Claude, there is simply no substitute, and the gap there is still significant.

But most of an organization's usage is not development. It is writing, summarising, document analysis, internal support and working with sensitive information. All of that can move to a model running on your own hardware, leaving the expensive models for the tasks and the people who genuinely need them. And even there, you would be surprised how many development companies run on a 20-dollar-a-month Pro plan. Just remember to switch off training on your data.

If you are not sure where to start, feel free to get in touch.