The market for generative AI (GenAI) seems to evolve on a daily basis, but the providers of these applications nonetheless face some fundamental challenges. One of these is the choice of pricing model. In this week’s guest contribution, our colleagues Jacob Konikoff and Saran Rajendran describe the rationales and risks behind the options for pricing GenAI tools.
Users or Usage: What drives GenAI’s value?
Many industry observers were surprised by the affordable “per user” licensing introduced with recent GenAI products such as Microsoft Copilot and Salesforce’s Einstein for Service.
It seemed more intuitive that these companies would follow OpenAI’s lead and charge customers based on their usage. Usage-based models align a supplier’s pricing directly with their costs. This mitigates the risk posed by “super users,” who generate high costs but need to pay as they go. Such models can also make sense in the early stages of a technology, when stable usage patterns have not yet emerged, making it difficult to work out a fair average price per user.
But what if the source of value is augmentation of a user’s role? Let’s say, for example, that GenAI can make a team member 40% more productive. In that case, pricing by user is a logical approach, because it links directly to the value produced by the end user. More importantly, user-based pricing facilitates rapid market expansion, because it monetizes the existing user base and overcomes a major downside of usage-based pricing—the limitation on experimentation.
Usage-based pricing models can slow down experimentation because they incentivize users to be cautious with their usage, to save on costs. We highlighted that potential risk in our recent article on pricing strategies for GenAI. The less consideration companies devote to pricing strategy and pricing models, the greater the risk that they will artificially limit the potential of their solutions by discouraging customers from experimenting.
These scenarios raise some questions: When is it appropriate to charge by usage, by user, or even based on outcomes? A misstep could spell disaster, such as when a company charges per user when its services may one day render those users unnecessary.
Correctly determining the pricing model is complex and necessitates a nuanced understanding of value differentiation, the trajectory of costs, and the strategic pricing game being played. More foundational models lend themselves to the usage-based pricing in the Cost Game or Uniform Game. User-based pricing in the Value Game or Choice Game is a better fit for GenAI models that use proprietary data and interfaces, deliver differentiated value, and face limited competition.
Ultimately, the pricing model that a company chooses reveals how it wants to monetize and distribute the value it generates. The winners of tomorrow in GenAI won’t just have superior technology; they will also have made strategic investments in finding the optimal way to share value.
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