I used to think AI compute pricing was a subscription problem.
Pay monthly. Get access. The model runs whenever you need it. Unused capacity disappears into the billing cycle and nobody thinks about it.
Then I started asking what I was actually paying for.
Not compute I used. Compute I might use. Reserved access to infrastructure someone else controls, priced in a way that makes the cost invisible until the invoice arrives.
The subscription model was never designed for transparency. It was designed for predictable revenue. Metering came later, layered on as a premium tier, as a usage dashboard, as an opt-in cost alert that still doesn't tell you what each inference actually cost.
Every single one of those layers can be adjusted by the same company that set them. What can't be adjusted is a protocol that charges per inference at the moment of execution.
That's what @OpenGradient s x402 inference payment does. settles before compute runs. No subscription. No reserved capacity. No invoice that arrives after the fact.
I still don't know whether per-inference pricing survives at scale without introducing its own friction. The gap between a clean payment model and one that works under real load is where most of these ideas get complicated.
But I've stopped believing another billing dashboard is the answer.
#opg $OPG $LAB
Pay monthly. Get access. The model runs whenever you need it. Unused capacity disappears into the billing cycle and nobody thinks about it.
Then I started asking what I was actually paying for.
Not compute I used. Compute I might use. Reserved access to infrastructure someone else controls, priced in a way that makes the cost invisible until the invoice arrives.
The subscription model was never designed for transparency. It was designed for predictable revenue. Metering came later, layered on as a premium tier, as a usage dashboard, as an opt-in cost alert that still doesn't tell you what each inference actually cost.
Every single one of those layers can be adjusted by the same company that set them. What can't be adjusted is a protocol that charges per inference at the moment of execution.
That's what @OpenGradient s x402 inference payment does. settles before compute runs. No subscription. No reserved capacity. No invoice that arrives after the fact.
I still don't know whether per-inference pricing survives at scale without introducing its own friction. The gap between a clean payment model and one that works under real load is where most of these ideas get complicated.
But I've stopped believing another billing dashboard is the answer.
#opg $OPG $LAB