During prototyping, calling an LLM feels almost free. A few cents here, a fraction of a penny there then it barely registers. You're focused on getting the demo working, not on the bill. But the moment you decide to switch from "cool prototype" to "production feature serving real users" the economics shift dramatically. (Also I want to say that this article doesn’t cover cost calculations for local LLMs (self hosted). I’ll write about that later.)
The Math Nobody Does Early Enough
LLM providers charge per token which is roughly 0.75 words per token. A high-end model might cost you $0.01 to $0.03 per 1,000 input tokens. Its very cheap right? Let's see what happens when you do the multiplication.
Common example : Let's try AI Resume Screener (one of the most common projects I'm seeing in these days in linkedin)
Imagine you're building a hiring platform. Your flagship feature is an AI-powered resume screener that reads a candidate's CV and generates a structured evaluation like fit score, red flags, key strengths, and a recommended interview focus area.
Below is what a single request looks like.
Input side
That's 8,000 tokens in per screening.
Output side
Now let's price it. At $0.02 per 1,000 input tokens and $0.06 per 1,000 output tokens (typical for a capable model), one resume screening costs about:
> (8,000 × $0.02 / 1,000) + (800 × $0.06 / 1,000) = $0.16 + $0.048 = ~$0.21 per resume
Twenty-one cents. Still sounds trivial.
Now Scale It (What most people forget to calculate)
A mid-sized recruiting firm screens 500 resumes a day across all open positions. That's,
And that's just one feature. Add in follow up queries ("compare these three candidates"), re-screening when job descriptions change, and the occasional retry on malformed outputs - you're easily looking at $50,000+ per year just on inference costs for a single AI feature.
What I want to say?
Here's the part that stings, after spending all that money, you own nothing. You're not buying software. You're not building an asset. You're renting intelligence on every single API call, and the meter never stops running.
If your platform charges recruiters $99/month and each recruiter screens 40 resumes a day, that recruiter alone costs you ~$8.40/day in inference - about $252/month just in LLM costs. You're losing $153 on every paying customer before you even account for infrastructure, storage, or engineering salaries.
Why This Matters for Developers?
The token tax doesn’t mean you shouldn’t build with LLMs. It means you need to think about cost architecture from day one not after launch. A few strategies I use that might help you are,
Overall Thought
Prototyping with LLMs is deceptively cheap. Production with LLMs is deceptively expensive. The gap between those two scenarios is the token tax and it catches more teams off guard than any other cost in the AI stack.
Before you commit to an LLM-powered feature, do the multiplication. Take your per-request token count, multiply it by your realistic daily volume, and look at the annual number. If that number makes you uncomfortable, good. That discomfort is the starting point of a sustainable architecture.