๐ฌ AINFTโs Billing Transparency โ When AI Usage Becomes Verifiable, Not Guesswork
Most AI platforms hide the most important layer:
How you are actually charged.
You only see:
โ โ$20/monthโ
โ โcredits deductedโ
โ or a vague usage number
But you never see:
โ how many tokens were consumed
โ how much reasoning happened
โ whether cache was used
โ what you truly paid for
Thatโs Web2 logic.
Opaque. Black-box. Trust us.
AINFT takes the opposite approach.
It treats billing like on-chain data โ fully observable, fully auditable.
And that changes user psychology completely.
Letโs break down why this design feels so different ๐
1๏ธโฃ Deep Thinking โ Reasoning Is Separately Metered ๐ง
โ Deep Thinking: 124 tokens
โ Text Output: only 9 tokens
This detail is extremely important.
It proves the system is using:
โ chain-of-thought
โ reasoning steps
โ internal inference cycles
Similar to:
โ GPT reasoning models
โ Claude advanced reasoning
โ Gemini Pro logic passes
In most platforms, this creates a frustrating experience:
โ โWhy did I pay so much when the answer is only 1 sentence?โ
Because the thinking cost is hidden.
AINFT does something rare:
It explicitly shows:
โ how much the model thought
โ not just what it said
So the cost becomes:
compute-based, not output-length-based
This is honest engineering.
Youโre paying for brainpower, not word count.
2๏ธโฃ Cache Status โ Enterprise-Level Cost Logic โ๏ธ
โ Uncached Input: 57 tokens
This means:
โ the request did NOT hit cache
โ full computation was required
โ therefore full cost applies
But the implication is bigger.
If future queries:
โ reuse context
โ repeat prompts
โ reference the same long document
Then:
โ cache hits
โ lower compute
โ lower cost
This is exactly how:
โ cloud computing
โ database systems
โ enterprise APIs
optimize expenses.
AINFT is applying infrastructure-grade billing logic, not consumer flat pricing.
Thatโs a very Web3 + engineering mindset.
#TRONEcoStar
#AINFT @Justin Sunๅญๅฎๆจ