Company-wide ChatGPT, self-hosted on Windows Server

Right now, someone on your team is pasting a draft contract, a customer list or next quarter's figures into the free ChatGPT. Not out of malice. They have work to finish and nobody handed them a sanctioned tool. That text now sits on a server you do not control, under terms no one in your company ever signed.

You can keep sending the "please do not use ChatGPT" emails, or you can give the whole company one AI they are actually allowed to use, on your own Windows Server, with every major model and a bill you can predict. Here is how.

"Just buy ChatGPT Enterprise" costs more than the invoice

The per-seat model looks simple on paper: a licence for every employee, billed for the year. Then you meet the catch. You pay the same for the person who sends two prompts a month as for the one who lives in it. Seat minimums push you to buy more licences than you use. And every word your staff type goes to a US cloud, to one vendor, with one family of models.

For a regulated company, or simply a careful one, that last point is the real cost. Your most sensitive text leaves the building and you have nothing on paper that says where it went.

What "company-wide AI" should actually mean

One place where the whole team reaches every model that matters, Claude, GPT, Gemini and the rest, under a single login. You decide who gets access, who gets which models, and how much each person may spend. The data stays on hardware you own. And it is one integrated system, not five subscriptions taped together. It is the same idea as every AI model in one app, scaled to the whole company.

That is what kral is: a self-hostable AI platform that puts the models, the user management, the cost control and the chat in one stack you run yourself.

It runs on the stack you already have

kral is an ASP.NET Core application. It installs on Windows Server behind IIS, the same way your line-of-business apps already run. No Linux detour, no new platform your admins have to learn before week one. If your shop is Microsoft, the AI platform speaks your language.

That matters past install day. Patching, backups, monitoring, the Windows tooling your team already uses covers kral too. It sits inside your network, on your domain, behind your firewall.

Your own apps can use it, with one key

The chat is only the front door. Underneath, kral is a gateway: a single endpoint that talks to every provider. Point your internal tools at it, an invoice classifier, a support assistant, an Outlook add-in, and they all reach AI through one address with one key.

For a .NET team this is the familiar part. Your existing HTTP client points at one base URL with one service key, instead of scattering OpenAI, Anthropic and Google keys across a dozen projects. Every call is logged and billed against the same central budget, so finance sees one number and security sees one door.

Or keep a model entirely in-house

Every model in kral is just a provider with an address. That includes one running on your own hardware. Point kral at an OpenAI-compatible endpoint on your network, Ollama, vLLM or LM Studio on your own GPU, and add it like any other model. For the prompts you never want to leave the building, the request then goes to your server and stops there, no external API in the path at all.

Most teams mix the two: the big cloud models for everyday work, a local model for the handful of cases that must stay fully offline. You decide per model, per team.

One login for the team, and you hold the controls

Staff sign in once, through single sign-on that your IT runs. From the admin side you create users and teams, switch individual models on or off, and set a monthly spending cap per person. Someone burns through their budget on a heavy week? You see it, and you decide what happens next. Nobody is quietly running up a bill in a tool you cannot see.

A bill per token, not per seat

You pay for what people actually use, measured per token, not a flat licence per head. The colleague who asks three questions a week costs you almost nothing. You set the budget, kral measures every request against it and stops at the line you drew. EU VAT handling is built in, so billing does not become a second project.

Per-seat SaaS vs a platform you run

	Per-seat SaaS	kral (self-hosted)
Where your data lives	The vendor's US cloud	Your own server
What you pay for	A seat per employee, billed yearly	Actual token usage, with a budget you set per person
Models you get	One vendor's models	Claude, GPT, Gemini and more, plus local models on your own hardware
Runs on	The vendor's cloud only	Your Windows Server, inside your network
The chat interface	A fixed UI	Your white-label chat

"Will this really fit us?"

Three honest answers to the three questions you are already asking.

Is it compliant? The data stays on your server, in your jurisdiction. You can write down where every prompt goes, because it goes to hardware you own. That is the sentence your data-protection officer needs.

Are we technical enough? If your team keeps a Windows Server and an IIS site running, you have the skills. Installing kral is closer to deploying an internal web app than standing up a research cluster.

And honestly, when is it not for us? If you want zero infrastructure and you genuinely do not mind where the data lives, a per-seat SaaS is less work, and we will tell you so. kral earns its place when control of the data and the cost has to sit on your side of the wall.

AI for your profession

The same platform, framed for the work you do. Find the closest fit:

Regulated and confidential work

Teams protecting their own work and IP

Running AI across the company

Give your people the AI they are already trying to use, on your terms instead of a US vendor's. Book a short demo, see kral running, then talk to us about putting it on your own server.

Book a demo

Open the app

windows server ai platform dotnet enterprise ai self-hosted ai chatgpt alternative

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A company-wide ChatGPT that runs on your own Windows Server