AI providers don’t know what to charge in a constantly changing market

While it can seem as if artificial intelligence (AI) has all the answers, especially when given the right prompt, many providers are finding it difficult to find the right pricing models for their AI products in a rapidly evolving landscape.

Guy Marion, the chief marketing officer at Chargebee, a commerce platform for subscription and recurring revenue businesses, told IT Brew that companies building and iterating the latest AI products are experiencing market changes as much as every six months.

“The industry is moving at a faster pace than we’ve ever seen before, and the markets are bigger than they used to be,” Marion added.

In response, Marion said that some companies are choosing to charge customers on a subscription basis for using AI tools and services. While it could seem as if such fast industry development promotes competition and choice within the market, some see it as burdensome for consumers.

Chargebee, in a recent survey, reported that companies that combine subscription, usage, and outcome models (or the issuance of a flat fee) doubled their probability for increased profit margins in comparison to those with just a usage-based model.

Ch-ch-ch-changes. Given how much people are talking about AI, it’s sometimes easy to forget the newness of the technology and how rapidly it’s evolving. Even the big AI providers are trying to figure out…well, virtually everything about how to build and market AI products.

Michael Friedman, the SVP of customer experience AI-provider ASAPP, said that the easiest part of setting the price for an AI solution is talking to customers about the value of the model. But thanks to the AI industry’s constant change, it’s much harder to measure your product’s value against market competitors.

Tomás Hernando Kofman, co-founder and CEO of Not Diamond, a startup that builds infrastructure that allows companies to utilize multiple AI models, agreed. He said that companies that want to provide AI products and services can struggle with figuring out pricing because the unit economics for AI are different from software.

Deploying AI comes with inference costs, which is what a company spends to run a pre-trained AI model, including hardware and cloud expenses. Depending on the size of the model, the length and complexity of the AI’s output, and other factors, inference costs can quickly skyrocket, wrecking even the most carefully planned budgets.

“The inference cost is so high relative to the execution of traditional software programs and that means that people often end up in these very risky pricing models, where it’s like a subscription, flat fee, but you might end up underwater on that,” Kofman said.

Since Not Diamond uses traditional machine learning techniques in its products instead of AI models, the company’s unit economics aren’t feeling the pressure. Kofman said, however, that within the AI market, many companies are still in the investment stage, meaning they could be more focused on performance and quality than cost optimization; at least for the time being, they might be more willing to burn incredible amounts of cash to deliver a result.

“I do see a lot of enterprises, in particular, more comfortable running with poor, even negative, economics just to get their AI products to market,” Kofman said. “I think you’ll see more pricing changes in the space as well, both for AI-powered product prices to go up and for more and more companies to be looking for various cost optimization techniques to avoid ending upside down on the economics.”

Subscribe for more! Many providers are also experimenting with subscription pricing for AI models that stagger access and capabilities of a model rather than offering products at a flat fee.

Kofman said that the subscription model is “economically prohibitive for a lot of customers to have these subscriptions, which can get up into the hundreds of dollars per month” and offer less choice to customers who want to access different AI abilities for different projects.

Wired reported on ChatGPT Pro’s cost per month sitting at $200, with OpenAI’s CEO Sam Altman taking to X to share that he chose the price and believed the company would profit from it. Similarly, Anthropic’s expanded usage plans with varying flexibility sit in the $100-200 range and Google AI Ultra is available for $249.99 a month.

The Not Diamond founder isn’t the only one who is recognizing the market’s move to subscription models, especially as customers look to have multiple subscriptions across different platforms because they want access to these models. Marion pointed out that many pricing models across the larger market have been one-dimensional, offering a service at a standard fee per every payment cycle.

“Right now, we’re starting to move into fourth dimensions, where you’re looking at your plans, you’re looking at your price, you’re looking at your usage, and you’re looking at the value of that usage,” Marion said. “So that usage can get split into basic amounts and then higher value amounts, for example, AI-delivered solutions.”

Marion continued that he views the top performers in the AI space as currently “enhancing their pricing strategies in response to building and launching AI” through experimenting with pricing models.

Companies with the agility Marion described are trying to get profit margins up and become a player comparable to big box AI providers, like OpenAI, “and they’re losing an enormous amount of money because they’re consuming so much computer costs.”