Three ways to buy. One of them forecasts.
Time and materials, metered AI tooling, and a Koombea AI Pod are not the same purchase. The difference is who carries the risk when the estimate is wrong.
What you are comparing
| Dimension | Time and Materials | Metered AI Tooling | Koombea AI Pod |
|---|---|---|---|
| What you buy | Hours worked | Tokens, calls, or seats consumed | Delivered scope, measured in story points |
| Who carries estimate risk | You do | You do, and the meter runs either way | We do, on the scope you signed |
| What you can forecast | A burn rate, not an outcome | Neither reliably | A price and a scope, agreed upfront |
| What overruns cost you | More invoiced hours | More metered usage | Nothing, on the agreed scope |
| Quality accountability | Billed separately as QA | Yours to build and run | Generated tests and gates, inside the price |
| Reporting | Timesheets | Usage dashboards | Points consumed against items shipped, weekly |
| When it is the right choice | You own the backlog and want capacity | Your engineers want tooling, not delivery | You want an outcome at a known price |
The metered column is not hypothetical. Publicly listed engineering firms now bill AI delivery in supervised tokens, against a monthly minimum with a variable fee above it. It is a real model, seriously meant, and it is the exact opposite of ours.
The same work, three shapes
- Time and materials
- Monthly capacity commitment
- Fixed-bid AI Pod
The honest case for each
Time and materials is the right purchase when you own the backlog and genuinely want capacity you direct. That is a real need, and it is why we still offer Staff Augmentation as a separate model rather than pretending one shape fits everyone.
Metered AI tooling is the right purchase when you have engineers who want better tools. It is not a delivery model. The meter measures consumption, and consumption is not the same as shipped software.
That distinction is no longer academic. Some of the largest firms in this industry now sell AI delivery by the token. It is a considered bet by serious people, and it deserves to be read as a real alternative rather than a straw man. It is also the opposite bet to ours.
An AI Pod is the right purchase when you want a committed outcome and cost certainty before work begins. The tradeoff is that scope has to be defined well enough to price, which takes real work upfront.
One prices the input. The other prices the output.
Every pricing model answers one question: what are you counting? Hours count input. Tokens count input. Story points count output. That is the whole taxonomy, and everything else is packaging.
Moving from hours to tokens looks like a revolution. It is not. It swaps one input meter for another. The unit got smaller, faster and cheaper, and the risk did not move an inch. An AI meter is still a meter. It just spins faster.
Whoever counts the input carries none of the estimate risk. That is the part worth sitting with. If the work turns out to need three times the tokens, the meter simply reads higher. Nobody absorbs that. You do, the same way you did when it was hours.
Counting output is a much harder promise, which is why almost nobody makes it. It means sizing the work before agreeing the price. It means eating the difference when the sizing is wrong. A vendor only offers that if they have changed something real about how the estimate gets made.
That is why AI-First is a commercial position and not a technical one. It does not make us cheaper. It makes the estimate accurate enough that committing to it is survivable, which is what lets us sell the outcome instead of the meter.
- Hours count input. Tokens count input. Story points count output.
- The question to ask any vendor: if this takes twice the work you expected, whose invoice changes?
- On a meter, yours. On a point, ours.
Why the economics work at all
A fixed price on software delivery is usually a bet against yourself. Software projects overrun routinely, and a vendor who commits to a price is volunteering to absorb that. So the question worth asking is not whether we are confident. It is what we changed.
Two things. AI-First delivery compresses the expensive parts: specification, boilerplate implementation, test authoring, and regression coverage. And we estimate with ScopeGen AI, our own scoping tool, which turns a client conversation into an estimated work breakdown and is calibrated against what each project actually took to deliver.
That is the whole mechanism. It is not a discount, and it is not optimism about scope.
Where the confidence comes from
Bring us the backlog.
In 30 minutes, we will show you what a Pod would ship first and how we would price it.