Load, meter and ship AI on your own servers.

Put open models onto your own AI-capable servers in minutes.

• Your prompts never reach a public AI unless you decide they should. Chat and the models behind it run privately, on your own GPUs.
• One console shows utilisation, memory, power and temperature across the whole GPU fleet. You load and unload models from the same screen.
• A gateway of your own sits in front of the models, so every app and every person calls the same private endpoints.

Keep many models side by side and choose which one runs where.

• A model you rely on stays available, because open models are cached and versioned on your own storage.
• You decide when a model is loaded, swapped or retired. A change in a provider’s catalogue does not take one away from you.
• Several models run at once, each on the GPU you assign it to.

See which models are up, who uses them and what it costs. Every request is logged.

• Nobody has to guess what AI costs. Usage is backed by energy and request evidence, so the number is measured.
• Every AI prompt, sign-in and session is logged against a named person, in a form your auditor can read.
• You know a model is slow or down before your users do.
• Metering is there for your own planning. The subscription is per CPU socket, so a usage number never appears on your invoice.

Ready-made workstations for your developers, created automatically.

• A developer starts on Monday and is writing code inside your network the same morning.
• Windows and Linux workstations are created from a form and reachable only over your own private connection.
• You see who is connected, on which workstation, and what that machine is doing.
• Code and customer data stay inside. What leaves the building is pixels.
• ATAILA Coder, a coding agent in early access, works on your apps behind your firewall and on private models.

Attach your own documents to the models on your GPUs and, where you allow it, to public ones, with retrieval you configure and can audit.

• Ask your own documents and get answers with citations, so a person can check the source before acting on it.
• An AI helpdesk drafts the reply and waits for a person to approve it.
• You configure the retrieval and can audit it afterwards: which documents were used, and by which model.
• It works with the models on your GPUs. Where you allow it, a public model can answer too, under the same audit.

Create a web app in minutes and build it with AI. Every release from development to production is recorded.

• One form gives you a whole project: repository, build pipeline, database, storage, domain, certificate and monitoring.
• Production gets exactly the build you tested. Production opens only for a named person with a written reason.
• Your repositories, pipelines and container registry run on your own infrastructure.
• openmath.hu was provisioned this way, in minutes, and so was every project we run.
• After go-live, one board shows the health of the app, the path your traffic really takes, and which backups ran and which are stale. Visitor analytics are cookieless, with no third party involved.