The real cost of on-premise AI versus per-seat cloud assistants

Every business case for local AI eventually runs into the same question from finance: is this actually cheaper than just paying a cloud provider? The honest answer is that it depends, and anyone who tells you otherwise is selling something. So let me lay out the real maths, including the parts that do not favour us.

How cloud AI bills you

There are two meters running. Per-seat products charge a flat monthly fee for each user. Per-token APIs charge for the text going in and, more expensively, the text coming out. As of 2026 a mid-tier frontier model runs somewhere around two to three dollars per million input tokens and ten to fifteen dollars per million output tokens, going by the published rates from Anthropic, OpenAI and Google. There are also much cheaper small-model tiers, some down at ten to forty cents per million tokens, and for light workloads those are genuinely hard to beat.

That last point matters, so I will say it plainly. If a handful of people use AI occasionally, the cloud is probably cheaper than buying a GPU, and you should use the cloud. The economics of local AI do not turn on the simple case. They turn on volume.

Where the meter starts to hurt

Costs stop being trivial in three situations. The first is concurrency: lots of people using the assistant at once, all day. The second is retrieval over long documents, where every question drags a large chunk of context through the model and every token is billed. The third is agentic use, where the model calls itself in loops to complete a task, quietly multiplying the token count by ten or more. A single power user running agentic workflows can burn through hundreds of thousands of tokens a day. Multiply that by a department and the monthly invoice stops looking like a rounding error.

This is the profile where owning the hardware changes the picture, and it happens to be the exact profile of a company that wants AI woven into daily work rather than used as a novelty.

The break-even, honestly

There is a useful 2025 study on this, a cost-benefit analysis of on-premise LLM deployment, and its numbers are worth quoting because they are neither hype nor dismissal. A single modern consumer GPU can serve a 24 to 32 billion parameter model at roughly 150 to 200 tokens per second. For a small but steady workload, the authors find hardware paying for itself against commercial API costs in months, sometimes in under one. For larger deployments on data-centre GPUs, the payback stretches to a few years, and it only holds if you actually keep the hardware busy.

That caveat is the whole game. A GPU you bought and left idle is the most expensive way to run AI ever devised. The break-even assumes sustained utilisation, and independent analyses put the crossover point where self-hosting beats cloud at somewhere north of fifty percent steady usage. Below that, you are paying for silicon that naps.

The line item nobody budgets

Here is where most do-it-yourself on-premise projects quietly bleed money. It is not the hardware. It is the person. Running your own inference stack means someone has to keep models updated, GPUs healthy, drivers current and security patched. Analyses of on-prem total cost of ownership repeatedly find that a fractional infrastructure engineer, call it half a full-time role at seventy-five to a hundred thousand a year, is the single largest three-year line item, larger than the servers.

This is the honest weak point of naive self-hosting, and it is also precisely the problem a managed appliance exists to solve. When the box arrives pre-configured and someone else handles the updates and monitoring, that staffing line disappears from your budget. The distinction between "run your own models" and "own an appliance that runs your models" is mostly this line item.

The value that is not on the invoice

Cost comparisons that stop at tokens miss the reason most of our customers are here in the first place. When the model runs in your building, your data never becomes a transfer to assess, a breach waiting to be reported, or a dependency on a provider that can change its prices or its terms. IBM's 2025 breach research puts the global average cost of a data breach at 4.44 million dollars, and flags a real premium when unsanctioned AI tools are involved. None of that shows up in a per-token quote, but it is real money and real risk.

How to actually decide

Skip the vendor spreadsheets and do this instead. Estimate your steady-state usage in tokens per month, honestly, including the agentic and long-context workloads you plan to grow into. If it is small and likely to stay small, use the cloud and enjoy it. If it is large, or growing fast, or touching data you cannot send outside the building, price the owned option properly, including the staffing you will avoid with a managed appliance, and compare it to the cloud bill you would be signing up to pay every month, forever. For a lot of regulated, AI-hungry organisations, that comparison is not close. For others it genuinely is, and we would rather tell you so than win a deal you will regret.