The answer to your question is in your comment. There is a difference between tokens and credits. Tokens are a pricing metric. Credits are a translation between money and a pricing metric. You are absolutely right that credits do not have to be cost-plus.
Value IQ uses credits. And the pricing metric you're translating to isn't tokens. ValueIQ uses creating a deal, adding a product, adding a verified product, adding a team member, etc. as pricign metrics. At no point does ValueIQ sell tokens (that I can find).
Really enjoyed this. One nuance I’d add is that AI companies are not all solving the same pricing problem.
LLM owners (OpenAI, Anthropic, Google) have models that can do almost anything. When your platform spans thousands of use cases, tokens become the only common denominator across all of them. In that sense, token pricing is less a choice and more a consequence of having an extremely horizontal product. That said, even they are already experimenting with price skimming via model tiers and premium access to frontier models (e.g., Claude’s so many models with different API pricing)
What’s interesting is what the application layer is doing. Companies like Glean, Dust, and GitHub Copilot don’t own the underlying models, so many are introducing artificial currencies such as AI Credits, Flex Credits, or that sort. They’re no longer monetizing tokens directly, they’re monetizing different actions on their platforms not directly correlated with cost.
To me, that’s the first step toward outcome-based pricing. Not because they’ve figured out outcomes yet, but because they’re moving the pricing metric closer to how customers actually consume value.
This is Batin. I work on pricing and commercial strategy for an AI company, building monetization models for our AI products and enterprise applications.
I just started The Prices of Things to share some of the lessons, questions, and interesting pricing problems I encounter along the way.
Thanks, Batin. Feel free to connect with me on LinkedIn if you're there.
I just came up with a new analogy. The LLM owners are like electric utilities. You can do almost anything with an LLM or electricity. It's impossible to price for value. Agents are like electric appliances. They use the "utility" to deliver real and potentially measurable value.
Good analogy, and I agree. It reminds me of a Sam Altman quote you might find interesting if you haven't seen it:
"AI is a new utility, just like electricity or the internet. When electricity first arrived, companies didn’t sell people 'electricity'. They sold 'light at night'. We need to find our equivalent of 'light at night'."
On pricing, I think it's somewhat similar to insurance. Some customers get much more value than they pay for, others less, but overall the provider still comes out ahead.
Longer term, I wonder if the real product becomes the user profile itself. Personalized shopping, marketing, recommendations, and decision-making could end up being more valuable than the LLM interaction. In that sense, today's LLMs may just be the hook for building those future businesses.
I wonder if this is too general. There are agents where the value is well understood, I would argue that is true of the valueIQ agents, especially the Value Sales agent. I would also differentiate between tokens and credits. I agree that tokens are a cost plus approach, credits do not need to be. One can map a credit to value and indeed that is a core part of the credit pricing design approach that Michael Mansard and I are working on.
The answer to your question is in your comment. There is a difference between tokens and credits. Tokens are a pricing metric. Credits are a translation between money and a pricing metric. You are absolutely right that credits do not have to be cost-plus.
Value IQ uses credits. And the pricing metric you're translating to isn't tokens. ValueIQ uses creating a deal, adding a product, adding a verified product, adding a team member, etc. as pricign metrics. At no point does ValueIQ sell tokens (that I can find).
Really enjoyed this. One nuance I’d add is that AI companies are not all solving the same pricing problem.
LLM owners (OpenAI, Anthropic, Google) have models that can do almost anything. When your platform spans thousands of use cases, tokens become the only common denominator across all of them. In that sense, token pricing is less a choice and more a consequence of having an extremely horizontal product. That said, even they are already experimenting with price skimming via model tiers and premium access to frontier models (e.g., Claude’s so many models with different API pricing)
What’s interesting is what the application layer is doing. Companies like Glean, Dust, and GitHub Copilot don’t own the underlying models, so many are introducing artificial currencies such as AI Credits, Flex Credits, or that sort. They’re no longer monetizing tokens directly, they’re monetizing different actions on their platforms not directly correlated with cost.
To me, that’s the first step toward outcome-based pricing. Not because they’ve figured out outcomes yet, but because they’re moving the pricing metric closer to how customers actually consume value.
Excellent comment. I agree completely. Who is this?
This is Batin. I work on pricing and commercial strategy for an AI company, building monetization models for our AI products and enterprise applications.
I just started The Prices of Things to share some of the lessons, questions, and interesting pricing problems I encounter along the way.
Thanks, Batin. Feel free to connect with me on LinkedIn if you're there.
I just came up with a new analogy. The LLM owners are like electric utilities. You can do almost anything with an LLM or electricity. It's impossible to price for value. Agents are like electric appliances. They use the "utility" to deliver real and potentially measurable value.
Good analogy, and I agree. It reminds me of a Sam Altman quote you might find interesting if you haven't seen it:
"AI is a new utility, just like electricity or the internet. When electricity first arrived, companies didn’t sell people 'electricity'. They sold 'light at night'. We need to find our equivalent of 'light at night'."
On pricing, I think it's somewhat similar to insurance. Some customers get much more value than they pay for, others less, but overall the provider still comes out ahead.
Longer term, I wonder if the real product becomes the user profile itself. Personalized shopping, marketing, recommendations, and decision-making could end up being more valuable than the LLM interaction. In that sense, today's LLMs may just be the hook for building those future businesses.
I wonder if this is too general. There are agents where the value is well understood, I would argue that is true of the valueIQ agents, especially the Value Sales agent. I would also differentiate between tokens and credits. I agree that tokens are a cost plus approach, credits do not need to be. One can map a credit to value and indeed that is a core part of the credit pricing design approach that Michael Mansard and I are working on.