Services · Private LLM
Your own LLM.
Your own data.
Run open-source models - Llama, Mistral, Qwen - on your own EU infrastructure, with an OpenAI-compatible API so your app barely changes. Data sovereignty for regulated teams, real cost savings at volume, and no vendor lock-in.
When self-hosting wins
Data sovereignty
Healthcare, legal, finance, and public sector where data must provably never leave your control. Pairs with GDPR and the EU AI Act.
Cost at volume
Past a steady request volume, owned or reserved GPUs undercut per-token API pricing. I model the crossover for your traffic.
Control and stability
No rate limits, no forced model deprecations, no policy changes breaking your product overnight.
Honest fit check
If a hosted API is genuinely the better call for you, I will say so. Self-hosting is a tool, not a religion.
What the deployment includes
Model serving
vLLM or TGI with batching and an OpenAI-compatible endpoint, so your existing client code mostly just works.
RAG over your docs
A private retrieval pipeline with EU-hosted vectors, hybrid search, and reranking.
Model selection
Benchmarked on your data, not a leaderboard. Right-sized for your latency, quality, and GPU budget.
Evals and monitoring
Quality evals, latency and cost dashboards, and GPU utilization tracking.
Pricing
| Scope | Timeline | Price |
|---|---|---|
| Private endpoint proof-of-concept with RAG | 2-4 weeks | $12K-$30K |
| Production deployment with autoscaling, evals, monitoring | 5-9 weeks | $30K-$60K |
| Hourly retainer post-launch | Ongoing | $100/hr |
Prices are for engineering. Your GPU and infrastructure spend is separate and billed by your provider.
Frequently asked questions
Why self-host an LLM instead of using OpenAI or Claude?
Three reasons: data sovereignty (the data never leaves your infrastructure), cost at scale (past a certain volume, owned GPUs beat per-token pricing), and control (no rate limits, no model deprecations, no surprise policy changes). For many EU teams in healthcare, legal, finance, and government, the first reason alone makes it non-negotiable. For most others, a hosted API is still the right call - I will tell you honestly which camp you are in.
Which open-source models do you deploy?
Llama, Mistral and Mixtral, Qwen, and DeepSeek depending on the task, plus embedding models like BGE and E5 for retrieval. I benchmark a few candidates on your actual workload rather than picking by leaderboard, because the best model for your data is rarely the biggest one.
Where does it run?
On-premise on your own hardware, in your private cloud, or in an EU region of a provider you already use (Hetzner, OVH, Scaleway, AWS, Azure, GCP). The point is that you choose, and personal data stays where your compliance team needs it. This pairs directly with GDPR and EU AI Act requirements.
What does the deployment include?
Model serving (vLLM or TGI), an OpenAI-compatible API so your app code barely changes, autoscaling and GPU sizing, a RAG pipeline over your documents if needed, evaluation, monitoring, and cost tracking. You get a private endpoint that behaves like a commercial API but runs on your terms.
Is a self-hosted model as good as GPT or Claude?
For general reasoning, the best closed models still lead. But for a focused task - classification, extraction, domain Q&A, summarization over your own data - a well-chosen open model with good retrieval is often indistinguishable in quality and far cheaper at volume. I am candid about where the gap is real and where it is not.
How much does a self-hosted LLM deployment cost?
A proof-of-concept private endpoint with RAG is typically $12K-$30K. A production deployment with autoscaling, evals, and monitoring runs $30K-$60K, plus your infrastructure spend. Scoped after a free 30-minute call.