Predicting AI Cost is turning out to be a actual nightmare!
Our AI bill was 3x what we estimated. Not because we picked the wrong model - we just didn't know what we didn't know.
Turns out token price cards are the tip of the iceberg. Reasoning models bill internal chain-of-thought tokens at full output rate.
Knowledge base, Prompt Size, Tool calling, and Context quietly add hundreds of system tokens per request.
Real-time audio is priced in a completely different unit than text on the same model.
And that's just LLMs.
STT providers round audio duration differently, and it matters at scale.
Agentic loops that trigger web search can quietly add thousands of API calls nobody budgeted for.
PS: different models cost differently, and the cost can be changed anytime.
Genuinely curious how others are handling this. Some tool for help?
Are you estimating upfront or just reacting to the invoice? List of parameters to keep an eye on?
And what's the cost variable that caught you most off guard?
Replies
Bababot
Curious whether anyone here has built internal dashboards specifically for token attribution by feature/workflow instead of only provider level billing?
I guess it depends on what the LLMs are used for - if we're talking development, then we're just monitoring the usage daily and trying to predict the spendings for a longer period (week/month), then deciding if we need to switch to another (potentially cheaper) model or maybe use LLMs less
Kipps AI
@sk_uxpin, That might not be a good idea for a team/company that is checking the project feasibility.
Also might not be good for a company where there is a need to create AI agents daily based on requirements from customers. Customer needs the price before the pilot even!
@nishit_chittora agreed about the customer flow - not so much about development flows, really. I'd say that it depends a lot on specific use cases