Self-Hosted AI
Run open-weight models inside your own network, on hardware you own. Your data, your code, and your customers’ information never leave the building. None of it is ever used to train someone else’s model.
We spec the hardware, connect your data, pick and tune the models, and lock down access. You end up with private AI you control, usually for less than you’re paying API providers today, and tuned to your work rather than everyone’s.
Best for
Teams spending $5,000 or more a month on cloud AI, with data or IP that can’t leave their walls.
Timeline
4 to 8 weeks, spec to handoff.
Investment
Fixed scope and price after discovery. Services from $20,000. Hardware quoted separately.
Why own your AI?
When your AI runs on your hardware, nothing about it belongs to a vendor.
- dns Nothing leaves your network. Every prompt and answer runs on hardware you own. No third party sees your data, and none of it trains someone else’s model.
- wifi_off Air-gap when you need to. For sensitive workloads, your AI can run with no path to the internet at all.
- verified_user Cleaner security reviews. For SOC 2, HIPAA, and data residency requirements, “it stays in-house” is the simplest answer.
- payments Off the token meter. Cost doesn’t climb with usage. No per-token bill, no per-seat fee.
- tune You control the model. Open models, tuned on your data, that get better at your work instead of everyone’s.
- link_off No vendor lock-in. Models don’t get deprecated out from under you and prices don’t jump. You upgrade on your own schedule, not theirs.
What we handle
- GPU servers spec’d and sourced for your workload, on-prem or in your colo
- Open models installed and tuned on your data
- Your documents, code, and databases connected, so answers are grounded in your own material
- An OpenAI- or Claude-compatible endpoint, so existing apps and agents plug in without a rewrite
- Single sign-on, network isolation, and an audit trail
- Optional onboarding, documentation, and support after handoff
How it works
- Talk A free discovery call about what you run now, what it costs, and whether owning your stack would pay off. If cloud is still the right call for you, we’ll say so.
- Scope & quote A written plan, a hardware recommendation, and a fixed price you approve before anything gets built. Hardware is quoted as a separate line item.
- Build, tune, hand off We install on your hardware, connect your data, tune the models, and onboard your team. Support continues after go-live for as long as you need it.
From $20,000
Typical build is $20k to $50k depending on models, data, and scale. Hardware quoted separately.
What you get
- A private AI system your team actually uses, on hardware you own
- Your existing apps and agents, unchanged, now pointed at your own server
- Models picked and tuned for your work, yours to swap or retrain later
- One less thing to explain in a customer security review
- Spend you can put in a budget, not a meter that climbs with every prompt
- A team that can run it without us, backed by real docs and hands-on training
Who this is for
- payments Teams spending $5,000 or more a month on cloud AI
- shield Teams with customer data, source code, or trade secrets that shouldn’t sit on a vendor’s servers
- tune Teams that want a model tuned to their work, not a general-purpose one everyone else is using
- block Not for you if AI is a few prompts a week. Stay on cloud.
Common questions
Is an open model good enough, or should we stay on the big cloud models?
For most workloads—drafting, summarizing, search over your own data, and coding help—open models on your own hardware match the cloud models, and a tuned one often beats them on your specific tasks. The big cloud models can still win on open-ended frontier reasoning. We benchmark against your actual work and recommend a mix when that’s the smarter answer.
Will our existing apps and agents still work?
Yes. We expose an OpenAI- or Claude-compatible endpoint, so code, agents, and tools written for cloud APIs point at your server with a URL change instead of a rewrite.
Does any of our data leave the network?
No. Inference runs entirely on your hardware. Prompts and answers never go to a model vendor or the cloud, and none of it trains anyone else’s model.
Can it run air-gapped?
Yes. For workloads that require it, the system runs with no internet connection at all.
Does this help with SOC 2, HIPAA, or a security review?
It removes the hardest question, where the data goes, by keeping it in-house. We set up access control and an audit trail and document the build, so there’s something concrete to hand an auditor. We’re not your compliance auditor, but owning the stack makes the answers simpler.
What hardware do we need, and can it serve the whole team?
An on-prem GPU server sized to your workload and how many people use it at once. We handle batching and serving so it holds up under real load, from a single workstation to a multi-GPU rack. You buy the hardware; we spec it, install it, and hand it off.
Can you tune a model on our own data?
Two ways. We connect your data for retrieval, so answers cite your own documents. And when the payoff is there, we fine-tune the model on your domain so it picks up your vocabulary and patterns.
How is this different from Custom AI Tools?
Self-Hosted AI is the private model and hardware your whole team runs on. Custom AI Tools is software built around one workflow. Plenty of teams do both.