Bounded AI Pricing (BAP): Fixing the cost problem no one wants to talk about
By Alan Jacobson, AI Economics Strategist
Everyone is focused on what AI can do.
Almost no one is focused on what AI costs to run.
That’s the problem.
The hidden break in the SaaS model
SaaS worked because the economics were simple:
- build once
- sell many times
- cost doesn’t scale with usage
- margins expand
AI breaks that.
Every interaction has a cost.
Every workflow has a cost.
Every “improvement” in product experience increases compute load.
So the more value you deliver, the more cost you incur.
That’s not a product issue.
That’s a business model problem.
Why current pricing models fail
Most companies are trying to force AI into old models:
- Seat-based pricing → hides cost until margins collapse
- Token / usage pricing → exposes cost but creates anxiety and unpredictability
Neither works.
One breaks the provider.
The other breaks the customer.
The real requirement: cost predictability
Customers don’t need perfect pricing.
They need:
- predictability
- control
- no surprises
If a CFO can’t forecast it, they won’t approve it.
If a user can accidentally run up a bill, they won’t trust it.
Introducing Bounded AI Pricing (BAP)
Bounded AI Pricing is a simple idea:
AI usage must be bounded, visible, and controllable
It has three parts.
1. Fixed price per seat with included compute FLOPs, not tokens
Each seat includes a defined allocation of compute.
Not tokens – which are a poor proxy for compute. Not abstract units. Actual compute capacity, based on metered FLOPs, then normalized across all hardware and software platforms.
This aligns pricing with the real cost driver while keeping it predictable.
So customers know:
- What they are paying for
- What is included
- What it does
2. Fixed overage with early detection and warning
When usage exceeds the allocation:
- overage pricing is predefined and transparent
- customers receive real-time alerts before limits are reached
- soft caps and throttles prevent runaway usage
No surprise invoices.
No “we didn’t know” conversations.
3. Customer-controlled governance
The customer controls usage inside their organization via a new control dashboard, from the service provider, to:
- Set limits by user, team, or workflow
- Restrict high-cost operations
- Enforce policies before cost is incurred
- Prevent inference when pre-determined conditions are not met
- Route to less-expensive alternatives when appropriate
This is critical because AI and agentic AI introduce new failure mode: employees and agents can unintentionally generate cost at scale
Governance closes that gap with governed, pre-execution provisioning (G-PEP).
Why this works
For customers:
- predictable spend
- no surprise bills
- control over internal usage
For providers:
- cost recovery aligned to compute
- protection against unbounded usage
- ability to scale without margin collapse
The shift
This is not:
- seat pricing
- or usage pricing
It’s: bounded consumption with governance
The bottom line
AI doesn’t kill SaaS.
Unbounded cost does.
Who wins AI?
Not the company with the best model.
Not the company with the most features.
The company that makes AI cost predictable.
– Published Friday, March 20, 2026