"Private AI" sounds like something only banks and hospitals with a research division can pull off. It isn't. Open-weight models are now good enough, and cheap enough to run, that a mid-sized business can own its AI outright — data never leaving the network, no per-token bill, no third party in the loop.
What stops most companies isn't capability. It's not knowing which decisions matter and which are noise. Here's the shape of a private-AI deployment, minus the mystique.
First: do you actually need it private?
Plenty of workloads are perfectly fine on a hosted frontier model. Private AI earns its keep when one of these is true:
- The data can't leave. Regulated records, client confidentiality, or contractual terms that forbid sending information to a third-party API.
- The volume is high and steady. At enough usage, a fixed-cost machine you own beats a metered API that never stops charging.
- You want control. No model deprecations you didn't choose, no rate limits, no vendor reading your traffic.
If none of those apply, we'll tell you — and point you at a hosted option instead. Private infrastructure you don't need is just cost.
Choosing the model is a workload question
There is no single "best" open-weight model; there's the best one for a given job at a given size. The questions that actually decide it:
- What is the task — drafting, extraction, classification, code, chat?
- How long are the inputs, and how fast do answers need to come back?
- How good is good enough, measured on your examples rather than a leaderboard?
The honest comparison isn't "open vs. frontier." It's "the smallest model that clears your quality bar on your real tasks" — which is usually far smaller than people expect.
We benchmark candidate models against a set of your own examples before recommending one. A model that tops a public benchmark can still be wrong for you if your inputs look nothing like the benchmark's.
Sizing the hardware, honestly
This is where quotes tend to get vague. The variables are concrete: model size, how many requests you need at once, and how quickly each must return. Those determine the GPU memory and throughput you need — and therefore whether this is a single workstation-class box or a small rack.
We'd rather tell you a workload fits on modest hardware than sell you more than you need. Getting this right is the difference between a machine that pays for itself in a year and one that sits idle at 10% load.
Hosted or on-premise — and switching between them
There are two ways to run a private model, and you don't have to choose permanently:
- Hosted private — a dedicated model in isolated infrastructure we house, monitor, update, and support. Fastest to stand up, no hardware to buy, still fully yours and not shared.
- On-premise — the model runs on hardware inside your own network. Maximum control; data never crosses your boundary.
A sensible path is to start hosted to prove the workload, then migrate on-premise once it's earning its keep and you know the real usage. We build it so that move is a deployment change, not a rebuild.
Wiring it into real work
A model on its own is a very expensive text box. The value shows up when it's connected to the tools your team already uses — your documents, your ticketing system, your internal apps — with the same guardrails and approval points any automation deserves. That integration work, plus training the people who'll use it, is most of what makes a private deployment actually land.
You don't need to become an AI company
That's the whole point. Running AI in-house used to imply hiring researchers and standing up an ML platform. Today it's closer to any other piece of serious infrastructure: choose the right components, size them properly, integrate them, and keep them monitored.
That last part matters — a private model isn't set-and-forget. It needs the same care as any production system: updates, monitoring, and someone accountable when it misbehaves. You can own that in-house or have us run it for you.
If you're weighing whether a workload belongs in-house, a free audit is the cheapest way to find out — we'll size it, benchmark it against your data, and tell you plainly whether private is worth it.