Private AI infrastructure is the hardware, models, and plumbing that let your business run modern AI on your own servers — not someone else's — so your customer data never leaves your perimeter and your costs stop being a surprise.
Calling an AI provider's API is a fine way to start. It is also the wrong answer for a surprising number of real businesses. Your customer data leaves your network. Your bill climbs faster than the value you get back. You are tied to one provider's prices, outages, and the day they retire the model you depend on. And at some point your compliance people start asking questions nobody can answer.
The other path is to run AI yourself — on your hardware, in your cloud account, under your control. That is a different kind of work, and it is the work I do: designing and deploying private AI that matches what your business actually needs, instead of blindly copying what the big API does.
What can self-hosted AI infrastructure do?
Run a private ChatGPT for your team. An internal assistant that lives on your network, connected to your own documents and databases. Your staff get the everyday usefulness of a chatbot, and nobody outside your company ever sees the prompts.
Cut a runaway AI bill down to something predictable. If you are spending real money every month on AI and watching it climb, moving the bulk, repetitive work onto your own machines turns an unpredictable per-use charge into a steady, known cost.
Keep regulated data inside your walls. Health records, financial data, anything covered by a policy or a law — when the AI runs on your infrastructure, that data never makes a trip to a third party. Compliance gets simpler because there is less to explain.
Use the best tool for each job, automatically. Most businesses do not need one model for everything. A quiet piece of software in the middle sends each request to the right place — your own servers for the routine bulk, an outside provider for the genuinely hard cases — and your applications never know the difference.
Stop depending on a single vendor. When your AI is yours, a price hike, an outage, or a discontinued model is an inconvenience you route around, not a fire drill that stops your business.
What technology powers it?
I build on open, well-understood tools — nothing proprietary, nothing you cannot inspect or replace. Named briefly, with the plain reason each is there:
Inference engines — vLLM, TGI, Ollama, llama.cpp. These are what actually run a model on your hardware. The right one depends on the job: high throughput, simple setup, or running on ordinary CPUs.
Open models — Llama, Mistral, Qwen, and their peers. Small models for cheap, high-volume work; large models for hard reasoning. Picked per task, not by fashion.
A router — LiteLLM or a custom gateway. Your applications talk to it as if it were the usual AI API; behind the scenes it decides which model, on which machine, handles the request. This is what lets you swap models without rewriting your software.
Observability — Langfuse, OpenTelemetry, Grafana. The tools that let you see every AI request: what went in, what came out, how long it took, what it cost.
Standard infrastructure — Docker, Linux, GPU drivers, and a vector database such as pgvector when the AI needs to search your documents. Boring, proven pieces — the kind you can hire for and maintain for years.
What makes the infrastructure production-grade?
The difference between a demo and a system you can bet a business on is the unglamorous discipline around it.
You can see every request. Inputs, outputs, latency, and cost are logged and traceable from day one. When something looks wrong, you read the logs — you do not guess.
Costs are controlled, not hoped for. Per-team quotas, per-application budgets, and alerts when spend looks abnormal. The point of owning your AI is predictable cost, so the controls that keep it predictable are built in, not bolted on later.
Quality is measured. New model versions and prompt changes are checked against a set of your real examples before they go live, so a quiet regression gets caught by the test instead of by your customers.
There is always a fallback. If a self-hosted model cannot handle a request, or a machine goes down, the router sends that traffic to an external provider automatically. The business keeps running while you fix the underlying problem.
How does an AI infrastructure project work?
First, I look at what you are actually doing with AI. Which tasks, what volume, what it costs you now, and where your sensitive data flows. That tells us honestly what should move onto your own servers, what should stay external, and whether self-hosting even makes financial sense for you yet. This first conversation is free.
Then I design it and prove the hard part. I pick the hardware target and the model candidates, and design the routing, monitoring, and cost controls. Before anyone commits to a full build, I prototype the riskiest piece on your real workload so the plan rests on a measurement, not a hope.
Then I deploy and migrate carefully. The full setup goes up, and your existing AI work moves over gradually — running new and old side by side first, shifting a slice of traffic, then completing the switch. Monitoring is wired in from the start.
Then I hand off or stay on. You get the code, the deployment, and the runbooks — all yours. From there I can stay retained for model updates and tuning, or do a clean handoff to your own team. Your choice.
What you get
Private AI running on your infrastructure. A working deployment on your servers or your cloud account, serving real workloads — your models, your data, your machines.
A bill you can predict. Costs you can see per team and per application, with budgets and alerts, instead of an invoice that surprises you at the end of the month.
The ability to swap models freely. Because your applications talk to a router rather than to one provider, changing or adding a model is a configuration change, not a rebuild.
Everything handed over. Source code, deployment scripts, upgrade runbooks, and rollback procedures — all in your repository, on an open-source stack throughout. No proprietary platform of mine to depend on.
A team that can run it. I hand off clean, with documentation and a walkthrough, so the people who maintain your infrastructure can operate the result without me on call.
Is self-hosted AI infrastructure right for you?
A good fit if:
- You are already spending real money each month on AI and the cost is scaling faster than the value
- Data sensitivity or a compliance rule makes sending data to a third-party AI a genuine problem, not a theoretical one
- You want ownership — your models, your infrastructure, your data flows
- You have technical people who can operate the result once I hand it over
- You understand that running your own AI has an operational cost as well as a saving
- You want an honest advisor, not a vendor with a proprietary platform to sell you
Not a fit if:
- You want AI with zero operational overhead — then a managed API provider is the right product, and you should pay them
- Your AI spending is small — the economics of self-hosting do not work at a low scale, and I will tell you so
- You have no technical team at all — someone has to operate infrastructure, not just receive it
- You are shopping purely for the cheapest possible setup — I optimise for correctness and visibility, not raw price
- You want me to pretend a small open model will fully replace a frontier provider on hard reasoning — it will not, and I will say so up front
That last point is not a criticism — it is just being straight about where each tool belongs.
Frequently asked questions
Does self-hosting really save money compared to using an AI provider's API?
At scale, often yes — sometimes several times cheaper once it is running steadily. Below a fairly modest monthly spend, probably not worth it. I model the real costs honestly before you commit, and sometimes the right answer is "stay on the API for now and revisit in six months".
Which open models are actually good enough?
For most everyday business tasks, the leading open models are close enough to the big commercial ones at a fraction of the running cost. For the hardest reasoning work you will still want a frontier provider. I design for both, so each request goes to whichever makes sense.
Do I have to buy GPUs?
Not necessarily. There are three paths: your own hardware on-premise, which has the best economics at scale; rented cloud machines, which avoid the upfront purchase; or a mix of the two. I model all three against your situation before you commit to anything.
What about compliance — health, financial, or regulated data?
Running AI yourself usually makes compliance simpler, because the data never leaves your perimeter — there is less to explain and audit. I work with your compliance people on architecture documentation and data-handling. I am not a compliance lawyer, but I have built systems that passed audits.
Will this work with the applications we already have?
Yes. Most self-hosted setups present the same shape of interface as the common AI APIs, so your existing software barely changes. Where a closer integration is needed, I build it.
What if open models stall, or I want to leave?
You own the deployment, the code, and the infrastructure. If you ever want to move back to external APIs, the router already supports it — it is a configuration change, not a rebuild. There is no lock-in by design.
Let's talk
Bring your current AI setup — what you use it for, what it costs you now, and where it is starting to hurt. Thirty minutes, no deck, no sales — just a straight conversation about whether running your own AI makes sense for your business. The discovery call is free.