Quick answer
On-prem AI, also called on-premise, local, or private AI, runs artificial-intelligence models on hardware physically located in your own office instead of sending your data to a vendor's cloud. For a small firm it usually means a small dedicated machine on your network runs the AI, so client data stays in your building and you own both the hardware and the models.
If you run a small firm and you have looked at AI, you have probably hit the same wall: almost every tool wants you to upload your client files to someone else's servers. For a business whose whole reputation rests on confidentiality, that is a hard thing to do casually. On-prem AI is the answer to that problem. It is the same kind of AI you have read about, just running somewhere you control instead of somewhere you do not.
A plain definition
"On-prem" is short for on-premises, meaning on your own premises, in your own office. On-prem AI is an AI system that runs on a computer you own and keep in your building. When the AI reads a document or writes a draft, that work happens on the machine in the next room, not on a server farm you will never see. The opposite arrangement is cloud AI, where you send your text to a vendor over the internet, their computers do the work, and they send the answer back. Same capability, very different path for your data.
On-prem AI vs cloud AI
The difference comes down to four plain questions. Where does the model run, where does your data go, who owns the setup, and how do you pay for it.
- Where the model runs.Cloud AI runs on the vendor's servers. On-prem AI runs on a machine in your office.
- Where your data goes. With cloud AI, the content you process is sent out over the internet to the vendor. With on-prem AI in the default setup, the content stays on your network and is processed right there.
- Who owns it. Cloud AI is a service you rent, and it disappears the day you stop paying. On-prem AI is hardware and software you own and keep.
- Cost shape. Cloud AI is usually a per-message or per-seat subscription that climbs with use. On-prem AI is a larger up-front setup plus a flat monthly fee to keep it running, so heavy use does not run up a meter.
Where your content ends up can also matter for how it is reused. By default, OpenAI may use content from personal ChatGPT accounts to improve its models unless you opt out; its business and enterprise tiers are excluded (OpenAI Help Center). With on-prem AI in the default setup, the content never leaves your network in the first place.
The stakes on "where your data goes" are not abstract. In IBM's 2025 Cost of a Data Breach Report, the global average breach reached $4.44 million (IBM, 2025). The same report found that breaches involving shadow AI, the unsanctioned AI tools employees adopt without approval, cost an average of $670,000 more (IBM, 2025). Keeping the data on a machine in your office is one way to keep client files out of tools you never approved.
Neither is "better" in the abstract. Cloud AI is quick to start and fine for low-stakes content. On-prem AI is the right call when the data you would be sending is the exact data you are paid to protect. If you want the longer version of that, here is where your client data goes with cloud tools.
What "local AI models" means
The AI itself is a model, a large file full of learned patterns that turns your input into a useful output. A local AI model is one of these files that runs directly on your own machine. There are strong open-source models, built by the wider AI community and free to run, that are good enough for everyday office work. Because the model lives on your box, there is no per-message trip out to a vendor: the request goes to the machine in your office, the machine answers, and nothing about that exchange leaves your network. That is the technical heart of why on-prem keeps your data in your building.
One honest note. In the default arrangement, local models do all the work and no client content leaves your network. Some firms choose to add an optional cloud-assisted path for the hardest tasks. That is opt-in, it is spelled out in the engagement letter, and it is never on by default. If your setup uses it, that is a deliberate choice you made, not something that happens quietly.
What it runs on: the box in your office
On-prem AI does not require a server room. In our setup it is one small, dedicated machine that we install on your office network, sized for the work your firm actually does. It sits alongside the computers you already have, plugged into the same network, quietly running the AI. You can point at it. That physical presence is the whole point: the thing doing the thinking is in your building, under your roof, on your power.
What you can actually do with it
On-prem AI is not a science project. It is a working assistant. The private AI employee we install, named Paige, handles the routine back-office pile that eats your day. For a fuller picture, here is what an AI employee does all day. In short, Paige can:
- Draft replies to incoming email and messages in your own voice.
- Summarize long documents and tangled email threads down to what matters.
- Answer plain questions about your own files and hand back a citation to the source document, so you can check the answer.
- Process intake and enter data so it does not pile up.
- Prepare follow-ups so nothing falls through the cracks.
The rule that sits over all of it: every outbound action is approved by a person first. Paige drafts and prepares; she never sends on her own, and the system enforces that. You stay the one who clicks send. This is the same private AI employee approach, just explained from the data-residency angle.
Who owns it
You do. The hardware is yours, the models on it are yours, and the credentials and accounts it uses are yours. This matters most at the moment people forget to ask about: what happens if you stop the engagement. With on-prem AI, the box stays in your office and keeps working. You are not renting access that switches off the day the invoice lapses. You keep the machine, the models, and your data, because all of it was already yours.
Is on-prem AI only for big companies?
That is the old assumption, and it is wrong now. On-prem used to mean a server room and an IT department, which is why only large companies bothered. The reason small firms can do it today is that the whole thing has shrunk to a single installed box and the open-source models that run on it are good enough for office work. The entire point of W&S is to turn that into a fixed, installed setup a small firm can own, without hiring anyone or standing up a data center. You do not need to be big. You need to care about where your data lives.
What it costs
The shape is simple and there is no per-message meter. It starts with an optional data residency assessment, from $500 to $1,500, where we map what data you have and where it currently goes. Then there is a one-time install from $3,000, done in person, with the hardware passed through to you at cost. After that it is a flat management retainer from $300 per month to keep the system updated and running. You can see the full breakdown on what it costs. Support is remote-first, with a 24 hour response and a next business day resolution target.
The short version
On-prem AI is real AI that runs in your office instead of someone else's cloud, on hardware and models you own, with a person approving every send. For a small firm that handles sensitive client work, that is the difference between using AI and worrying about it.