Two ways to buy the same AI capability — one bills every employee whether they use it or not, the other bills the work actually done
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Most AI buying decisions are argued on features and settled by pricing model. Per-seat licensing charges a flat monthly fee per named user. Usage-based pricing charges for tokens actually consumed. Owning a self-hosted platform charges for the GPU and nothing else.
The gap between them is not a discount — it is a different shape. Per-seat cost is a function of headcount. Usage-based cost is a function of work. Those two curves diverge the moment adoption is uneven, and adoption is always uneven.
In practice 10-20% of licensed users generate the large majority of AI activity. Per-seat billing charges full price for the other 80%. This page shows the arithmetic at three organization sizes, then covers when per-seat is genuinely the right call.
by ibl.ai and token-priced platforms
Pay for consumption or own the stackby ChatGPT Enterprise, Microsoft Copilot, Glean
Per-user subscription licensing| Criteria | Usage-Based / Owned | Per-Seat SaaS |
|---|---|---|
| Cost Driver | Tokens processed, or a flat license plus the GPU you run it on. Tracks the work performed. | Named users provisioned. Tracks headcount, which has no relationship to AI usage. |
| Cost of an Inactive User | Zero. A user who runs no queries generates no charge. | Full price. A license sits on the invoice whether or not anyone signs in. |
| Budget Predictability | Varies with usage; needs rate limits, routing, and monitoring to stay predictable. | Exactly predictable — headcount times price, known 12 months out. |
| Cost of Expanding Access | Turning AI on for everyone adds cost only for the people who actually use it. | Every additional employee adds full list price, which discourages broad rollout. |
| Criteria | Usage-Based / Owned | Per-Seat SaaS |
|---|---|---|
| 500 Users | Roughly $2,000-6,000/month of tokens at typical enterprise usage, or the cost of one GPU host. | $15,000/month at $30/user, $30,000/month at $60/user. Comparable enough that either can win. |
| 5,000 Users | Token spend grows with active use, not headcount — commonly 5-10x lower than the seat bill. | $150,000/month at $30/user; $1.8M/year before a single feature is evaluated. |
| 50,000 Users | Self-hosting amortizes: owned GPUs serve the whole population at a flat, capped cost. | $18M/year at $30/user. At this size the licensing model is the entire decision. |
| Cost of Low Adoption | A failed rollout costs almost nothing — you stop consuming tokens. | A failed rollout costs full contract value. You bought seats, not outcomes. |
| Criteria | Usage-Based / Owned | Per-Seat SaaS |
|---|---|---|
| Incentive Created | Encourages putting AI in front of everyone and measuring what gets used. | Encourages rationing licenses to a pilot group, which suppresses the value you bought. |
| Agent & Automation Workloads | Automated agents run without a human seat attached, so background work is priced normally. | Seat-based models fit poorly when the consumer of AI is a process rather than a person. |
| Vendor Leverage at Renewal | You can change models or providers; owning the platform removes renewal leverage entirely. | Your bill grows with your company and the vendor knows migration is expensive. |
| Time-to-Value | Requires infrastructure decisions, or a partner to deploy and operate it. | Buy licenses, assign users, start the same week. |
Usage-based and owned pricing bill the work: a 5,000-person organization where 700 people use AI daily pays for 700 people's worth of tokens, not 5,000 subscriptions.
Per-seat billing assumes every licensed user extracts equal value. Measured adoption is consistently top-heavy, so most of the invoice covers people who opened the tool twice.
Below a few hundred users the two are close. Past roughly 1,000 users the gap stops being a line item and becomes the budget.
Usage-based pricing carries variance. Without rate limits, routing, and monitoring, a runaway workflow can produce a surprising invoice.
Per-seat is a real product: fixed, forecastable, no infrastructure, no capacity planning, and finance can approve it in one line.
For small teams, short pilots, or organizations with no platform engineering capacity, per-seat is the rational choice — it is at scale that it becomes the wrong shape.
ibl.ai is licensed flat and self-hosted, so cost is bounded by the GPUs you run. Adding the ten-thousandth user changes utilization, not the invoice.
No managed per-seat vendor can offer this, because the seat is the unit they sell and their own costs scale with your usage.
Usage-based pricing fixes the inactive-user problem; owning the stack fixes the vendor-leverage problem too. ibl.ai is the option where both are solved at once.
Per-seat licensing at $30-60/user reaches seven figures annually before adoption is proven. Usage-based or owned pricing tracks actual work instead.
Under roughly 100 users the absolute difference is small, and per-seat SaaS needs no infrastructure, capacity planning, or platform staff.
Agents consume AI without occupying a seat. Usage-based or owned pricing is the only model that prices background automation coherently.
Universal access is the point of a platform rollout. Per-seat pricing makes universal access the most expensive possible choice.
Timeline: One to two months including a parallel-run billing cycle
Timeline: Days to a couple of weeks
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
ibl.ai removes the per-seat line from the equation: the platform is licensed flat and self-hosted, so the cost of serving your ten-thousandth user is the GPU it runs on, not another subscription. Agentic OS routes each task to the model that fits it on cost, latency, and capability, so the token bill reflects the work rather than the default of sending everything to the most expensive model. You own all the code and the data, deploy on any cloud, on-premise, or air-gapped, and can switch models without renegotiating a contract.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.