Private LLM deployment on your servers, with no data leaving your perimeter
We pick an open-weight model for your tasks, deploy it on vLLM inside your perimeter and expose an OpenAI-compatible API. Your apps switch to it without a rewrite.
What hurts
Your security team will not allow customer data to go to ChatGPT or other cloud models. So AI projects stall at the pilot stage. The business sees the value but cannot take it.
You already tried hosting a model yourselves. It answers slowly, falls over under load, and nobody knows whether it is really worse than GPT or just badly configured.
A bank or clinic has to explain to its regulator and auditor where data lives and who can access it. Under GDPR, a cloud API in another jurisdiction makes that conversation harder.
What we build
- Data classification: what a model may process and in which perimeter
- Comparison of 2-4 open-weight models on your real tasks
- Deployment on vLLM with an OpenAI-compatible API
- Hosting on your own server or a dedicated server in the EU (Hetzner, Germany)
- Authentication, rate limits and an access log for every model call
- Monitoring of latency, queue depth, GPU load and errors
- Cost comparison of a private model against cloud APIs
- Connecting one of your applications as the first consumer
What you get
- A running model with an API and access for your team
- A model comparison report based on your examples
- Deployment configuration in your own repository
- A monitoring dashboard with alerts configured
- A runbook for model updates and recovery after failure
How we measure the result
- Answer quality on your test set compared with a cloud model
- Response time and the number of concurrent requests the server handles
- Cost per thousand requests inside your perimeter
- Share of requests served without errors each month
Systems and stack
Industries
Questions
How much does private LLM deployment cost?
From $10,000 per project. The price depends on model size, the number of applications, redundancy requirements and whether you already own hardware. GPU rental or server purchase is billed separately. After discovery we give a fixed estimate within 2-3 days.
Will a private model be as capable as GPT?
Not always. On narrow tasks such as classification, field extraction or answering from a knowledge base, open-weight models often reach similar quality. On complex reasoning they can fall behind. We show this on your own examples before launch instead of promising it upfront.
What data and access do you need?
A set of 50-200 real task examples, access to a server or approval to rent one, and a contact in your security team. We sign an NDA and a GDPR DPA before any access. We operate under ISO/IEC 27001:2022.
How long does it take?
Usually 6–10 weeks. The first week goes to discovery and data classification. Then model comparison, deployment, load testing and launch on part of the traffic. You see a working result at a demo every week.
What happens after launch?
We can run the system under an SLA: model updates, monitoring and incident response. Or we hand everything to your team with documentation and training. The code and configuration belong to you.
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Describe the task. We reply within one business day, the first call is with the founder.
Describe the task. The first call is with the founder.
We reply within one business day. 30 minutes to tell you whether it is worth doing and what it costs.