Why we are building private AI you run yourself

Most teams meet AI the same way. They sign up for a service, paste in their questions, and pay for every word that comes back. It works, and for many tasks it works very well. But it comes with three costs that grow over time.

The first is money. When every prompt is metered, the bill rises with use. The more useful AI becomes to a team, the less predictable its cost gets.

The second is control. To get an answer, your code, your client files or your internal documents have to travel to someone else’s servers. For many businesses, that is a line they cannot cross.

The third is dependence. If the service changes its prices, its models or its rules, you change with it.

There is another way

Open AI models have become remarkably capable. Many of them run on an ordinary workstation, a well-equipped laptop, or a single server with a GPU. Excellent open-source tools exist to run them.

So why doesn’t every team do this already? Because running AI yourself, properly, is real work. Someone has to pick a model that fits the hardware, install it, keep it secure, put a key on the API, update it without breaking anything, back it up, and help colleagues when something goes wrong. Most teams do not have time for that.

That is the gap we are building Flowthera Server and Flowthera Studio to fill.

What we are building

Our mission is to make advanced AI easy to install, operate and govern on infrastructure that you choose and pay for yourself.

We earn from the software, updates and support. We do not resell AI usage, and we do not meter your tokens. When you run models on your own machines, there is no vendor token quota.

Every product will have a free tier that is genuinely useful. You should be able to install the desktop app, let it check your hardware, download a model and start working, without creating an account.

What we will not pretend

Running AI on your own hardware has real limits, and we would rather say them plainly:

• Models that run on a laptop are smaller, and less capable at some tasks, than the largest models offered by cloud AI companies.
• Speed depends on your hardware. A GPU makes a large difference.
• A server you reach over the internet keeps your data under your control, but it is online, not offline.
• “No token quota” does not mean infinite capacity. One machine can only answer so many requests at once.

We will publish measured results for real hardware and real tasks, with the method and the date, and we will keep a limitations page that tells you where the product falls short.

Where we are now

We are in a private pilot, installing and testing with a small number of organisations on their own hardware: software agencies, hosting and IT providers, and teams that work with large volumes of documents.

If that sounds like you, we would like to hear what you want to achieve. Request a pilot, or join the waitlist to hear when public builds are ready.