[nickhalstead_]

You're touching on what I think is the hardest unsolved problem in this transition. At DataSift we went through something similar - pricing firehose data access when the value was in the insights, not the volume. We ended up with a hybrid model.

My bet is we'll see "outcome pricing" win for most use cases - you pay for "site deployed" not "tokens consumed." But that requires the provider to absorb variance in AI costs, which is scary when models are still getting cheaper quarterly. The providers who figure out how to price on outcomes while hedging their compute costs will win big.

What pricing model is working best for you so far?

[novatrope]

At the moment we are concentrating on simply limiting maximum dollars spent. But like your thoughts. Gives me something to work on tomorrow during the holiday (US)

[nickhalstead_]

Smart approach honestly - cap the downside first, figure out the model later. That's basically what we did at DataSift early on too.

One thing worth thinking about as you iterate: track the delta between what customers would pay for the outcome vs what it actually costs you in compute. That gap is your real margin, and it'll shift fast as model costs drop. The companies that instrument that well early will be able to move to outcome pricing confidently when the time is right.

Enjoy the holiday - sometimes the best product thinking happens away from the keyboard.