The incentive is backwards
Per-seat AI tools charge you more every time another employee starts using the thing. Read that again. The pricing model punishes adoption. You spend a year convincing people to actually use the AI assistant, and your reward is a bigger invoice every month you succeed.
For a stable team of fifty, per-seat is fine. The math is predictable and the integration work is zero. We will say that plainly, because it is true. If your team is small and steady and your data is not sensitive, buy the seats and move on.
The problem is the other case, which is most growing companies: your headcount is climbing, your usage is climbing, and your most sensitive data is exactly what you want the AI to see. Per-seat pricing turns all three of those good things into cost.
The alternative: deploy it on your own cloud
Instead of renting seats, you deploy an agent harness onto your own AWS, Azure, or GCP. After that, the model is yours to run. You pay for inference - the actual tokens - and nothing per head.
The shape of the difference:
- Cost scales with usage, not headcount. Add a hundred employees and your bill does not jump by a hundred seats. You pay for the work the agents actually do.
- Your data stays in your boundary. Prompts, documents, and outputs run inside your VPC. They do not transit a vendor's servers. For a regulated team, that sentence is the whole conversation.
- You are not locked to one model. A good harness routes between providers - Claude, GPT, Gemini, local - so you are not married to whoever you signed with this year.
- You own the deployment. You can extend it, add tools, wire it into the systems you actually use. It is software you run, not seats you license.
We deploy this as a packaged engagement: the harness on your infrastructure, wired into Slack or Teams, with tools and guardrails tuned to your use case, starting at $5,000. After that it is your inference bill, not our invoice.
When per-seat still wins
To be fair to the other side: if you need broad, shallow fluency across a knowledge-worker population tomorrow with zero engineering involvement, a per-seat SaaS tool gets you there faster. There is no deployment, no infrastructure, no setup. That convenience is real and worth paying for in the right case.
The break-even shows up faster than people expect, though. Once a team is growing or the data is sensitive, the cost of deploying once on your own cloud beats the per-seat meter running every month.
If you are watching a per-seat AI bill climb and wondering when it stops, it does not stop, that is the model. If you would rather own the thing, book a call and we will run your actual numbers against a deployment.