I wrote about a practical approach to converting AI token usage into credits without floating-point arithmetic. The key insight is using multi-layered integer scaling (price scaling × credit conversion × per-million-token base) to maintain precision across millions of transactions.
I hate the current token gym membership pricing models, they are too complicated and abstract and even worse, you have issues like what this post points out. They favor the providers, not the users. They create a lot of stress for users who have to decide if they can afford to point GasTown at their codebase.
The problem is that the companies serving LLMs can't delineate compute between users. You're using a cloud of compute, not a single individual unit of compute. If you could measure at the unit of compute, then you'd just pay by the minute for what you use instead of by the token, we would get out of this situation.
I wrote about a practical approach to converting AI token usage into credits without floating-point arithmetic. The key insight is using multi-layered integer scaling (price scaling × credit conversion × per-million-token base) to maintain precision across millions of transactions.
I hate the current token gym membership pricing models, they are too complicated and abstract and even worse, you have issues like what this post points out. They favor the providers, not the users. They create a lot of stress for users who have to decide if they can afford to point GasTown at their codebase.
The problem is that the companies serving LLMs can't delineate compute between users. You're using a cloud of compute, not a single individual unit of compute. If you could measure at the unit of compute, then you'd just pay by the minute for what you use instead of by the token, we would get out of this situation.