Saturday, February 24, 2024
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Factors to consider before pricing AI-enabled SaaS

In 2019, I wrote a post on how companies should price their AI-enabled software. I focused on SaaS companies that were developing their own AI and highlighted pricing considerations as they work to improve their models.

Since then, there’s been a meteoric rise of third-party foundational model providers like OpenAI, MosaicML and more. These “AI as a service” vendors have enabled any SaaS player to integrate powerful AI into their application. This has created a mad dash to sprinkle AI pixie dust across the SaaS ecosystem. We’ve seen this among the countless newly minted startups and more established public companies.

The proliferation of this technology raises many questions, including how to deploy it safely, who will win (focused startups or incumbents with existing distribution?) and more. One important area that hasn’t yet been discussed much: how it should be priced.

Below, I lay out a working framework on how to think about pricing the AI in your SaaS application. The space is evolving rapidly, so I’ll update this thinking in future posts.

How much differentiated value do your AI features create?

By definition, these foundational models are accessible to every SaaS provider, so how should you think about pricing what is, in effect, a commodity you’ve integrated into your product? Start with first principles: How much differentiated value does this AI feature create?

By integrating AI features into the flow of your broader platform, you are saving the user time from having to leave their flow to go to the underlying model (ChatGPT, etc). Keeping the user in context can be a powerful unlock.

However, be honest with yourself as to how much value your AI is actually creating. Many AI features in SaaS today are getting a flood of initial tire kicks from curious users but aren’t seeing meaningful sustained adoption. Start by understanding retention and value creation.

SaaS companies should be solving for simplicity and adoption in their AI feature pricing. This is a time for learning and iteration.

 

Then ask yourself how differentiated your AI offerings are. If the majority of the value your AI feature creates can be garnered by going directly to ChatGPT, don’t try to make a significant margin on that feature. Reselling is not a sustainable value creation strategy (nor differentiation strategy, though that’s a topic for another post).

Even if you aren’t able to charge much for your AI features today, they can create meaningful value by making your current product more valuable and perhaps stickier. They can also be used to drive upsell to higher tiers, all of which can result in increased net dollar retention.

Over time, you can leverage initial features that may today just be a thin wrapper around a third party model to build more differentiated value (more on how below). When you get to that point, you can consider a more value extractive pricing approach.

AI SaaS pricing is in its early days

Factors to consider before pricing AI-enabled SaaS by Walter Thompson originally published on TechCrunch

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