Question 1

Why self-host an LLM instead of using OpenAI or Claude?

Accepted Answer

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.

Question 2

Which open-source models do you deploy?

Accepted Answer

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.

Question 3

Where does it run?

Accepted Answer

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.

Question 4

What does the deployment include?

Accepted Answer

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.

Question 5

Is a self-hosted model as good as GPT or Claude?

Accepted Answer

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.

Question 6

How much does a self-hosted LLM deployment cost?

Accepted Answer

It depends on your volume and how production-ready it has to be, and you get a fixed price in writing within three days of a free 30-minute call, before any work starts. A proof-of-concept private endpoint with RAG takes 2-4 weeks; a production deployment with autoscaling, evals, and monitoring takes 5-9 weeks. Your GPU and infrastructure spend is separate and billed by your provider.

Your own LLM.
Your own data.

When self-hosting wins.And when it does not.

Data sovereignty

Cost at volume

Control

Honest fit check

What I build.A private API that behaves like a commercial one.

Model serving

RAG over your docs

Model selection

Evals and monitoring

Call to launch.In weeks, not quarters.

Call

Scope

Build

Launch

I build every project myself,so I only take a few at a time.

A fixed price, in writing,
three days after the call.

Questions.Answered straight.

Pick a time.The call is free.

Your own LLM.Your own data.