On-premises AI
What on-premises AI actually costs
The hardware is the number everyone asks about and rarely the one that matters. Here's the whole picture, including the parts vendors leave out until you've signed.
Ask what self-hosted AI costs and you'll get one of two unhelpful answers. Hardware vendors quote you a machine. Cloud vendors quote you a monthly figure that looks tiny next to it. Neither is describing the actual decision.
The useful framing is that these are two different shapes of cost. Cloud AI is nearly free to start and grows with use. On-premises is a purchase up front and almost nothing afterwards. Which one wins depends on your volume — and if you're doing it for confidentiality, cost isn't the deciding factor at all.
The machine
For a small business this is smaller than people expect. You are not buying a data centre. In most cases it's one workstation-class computer with a modern GPU, sitting wherever your existing server lives.
The specification that matters most is GPU memory, because that determines which models fit. A rough guide:
| Setup | Suits | Typical use |
|---|---|---|
| Consumer GPU, 16–24 GB | A small team, one workflow at a time | Document extraction, drafting, classification for perhaps 5–10 people |
| Workstation GPU, 32–48 GB | Most small businesses | Larger models, several people working concurrently, search across internal files |
| Dual GPU or server class | Heavier or multi-site use | Many concurrent users, or the largest open models for harder reasoning work |
Most of the small businesses we talk to land in the middle row, and the machine cost falls in the low thousands of dollars. We deliberately aren't printing a precise figure: GPU prices move a great deal, and a number written today would be misleading within months. Get a current quote when you're ready to buy — and buy it in your own name, not ours.
You may not need to buy at all. If your constraint is "not a shared multi-tenant service" rather than "physically in our building," renting isolated hardware in your own name gets you most of the isolation with no capital outlay. That middle option suits a lot of businesses who assume they need the full on-premises build.
The part that actually costs money
Here's what the hardware conversation obscures: a machine running a model is not a working system. It's a machine running a model. The value comes from connecting it to the way your business already operates, and that is where the real spend sits.
- Integration. Connecting to your ERP, job folders, email, scanner, or accounting system. This is the bulk of any honest quote, and it varies enormously depending on how accessible your existing software is.
- Getting the behaviour right. A model that extracts the wrong field from your particular invoice layout is worse than useless. Tuning against your real documents — not samples — is where accuracy comes from.
- Deciding what humans still approve. Every system needs a boundary between what runs automatically and what waits for a person. Drawing it correctly takes judgement and a conversation, not a setting.
- Training the people who'll use it. Consistently the most underestimated line. A system nobody was shown how to use is the single most expensive outcome available to you, because you paid for all of it and get none of it.
Our own pricing reflects this shape: fixed-price projects starting at $1,500 for a single workflow, typically $4,000 for two or three connected ones with training, with hardware quoted separately at cost. We don't mark up machines, because we'd rather you buy the right one than the expensive one.
Running costs
This is where on-premises looks unusually good, and it's the part cloud comparisons quietly omit.
Once the machine is in place, using it costs electricity. There is no per-message fee, no token metering, no bill that grows when your team finally adopts the thing you bought. You can process ten thousand documents or ten, and the cost is the same.
Cloud pricing punishes success. The more your team uses it, the more you pay. Local pricing does the opposite — heavy use is exactly when it pays off.
Budget for a modest annual maintenance allowance: models improve and get replaced, operating systems need patching, and integrations occasionally break when the software on the other end updates. Someone has to own that — your IT person, an outside firm, or us on an as-needed basis. What you should refuse is a mandatory retainer. Nothing about a local system requires ongoing payment to keep running, and any vendor who structures it that way has designed a dependency rather than a handover.
When the cloud is genuinely cheaper
Often. We tell people this regularly and it costs us work, which is roughly the point.
If your usage is light or occasional, if nothing you're processing is confidential, and if you have no contractual restrictions on where data goes, a commercial AI service under a business agreement will cost you less and be running sooner. Setup is minimal, and the monthly bill for a small team's ordinary admin use is genuinely small.
In that situation, choosing on-premises anyway is a preference, not an economic decision. That's a legitimate reason — some owners simply don't want their work on someone else's servers, and that's their call to make — but it should be made with clear eyes rather than sold to you as savings.
Where the numbers actually flip
Three situations make local the cheaper option outright, not just the more private one:
- High steady volume. Processing documents continuously all day, every day, is where metered pricing accumulates and a fixed-cost machine wins.
- Whole-team adoption. Per-seat and per-use costs multiply across staff. A single machine serving twenty people doesn't.
- Compliance overhead avoided. The vendor assessments, contract reviews, and security questionnaires required to get a cloud service approved under a strict framework carry real cost in time and fees. Removing the vendor removes the assessment.
How to work out your own number
You don't need a spreadsheet. You need three answers:
- What work would this actually do — named workflows, not "AI for the business."
- How much of it touches material that can't leave — usually less than owners assume, and the split is where the plan comes from.
- What that work costs you today in hours, delays, or lost jobs. If nobody can put a number on this, the project isn't ready regardless of where it runs.
That's what our free audit produces: a ranked list of what's worth doing, a recommendation on where each piece should run, and a fixed price before any work starts. If the honest conclusion is that a cloud subscription serves you fine, you'll get that answer too — and it will have cost you an hour.
Related reading: AI without sending your data to the cloud explains how on-premises AI works and how to tell whether you need it. Self-hosted AI for manufacturers covers shops working under customer confidentiality terms.
Figures on this page are indicative and current as of August 2026. Hardware pricing in particular moves quickly — treat the ranges as a starting point for a quote, not a quote. M Kiln AI is based in Rochester, New York, and works with small and medium businesses across the United States.