Why a usage-based model is key to monetizing AI for SaaS

The artificial intelligence (AI) arms race is in full swing. Software as a service (SaaS) providers in every niche are exploring and rolling out AI offers. In fact, 77% of SaaS companies report that they have either launched or are planning to launch AI features. 

The urgency to not only launch but also effectively monetize AI-powered products and services is paramount. To be successful, your AI monetization strategy must achieve three things:

• Create and demonstrate value for the customer through a dynamic mix of prices, packages, and models
• Support the efficient monetization of that mix—from the offer all the way through to revenue recognition
• Evolve your mix of offerings to capture the full and changing spectrum of customer demand

For AI offerings, usage-based pricing provides the best avenue for accomplishing all of these goals. Let’s take a closer look at why. 

Why usage pricing works so well for AI

When it comes to AI, traditional flat rate pricing simply can’t scale quickly enough to match the evolving customer use cases and demands. But with usage-based pricing, your company can align monetization with the unique value proposition, cost structures, challenges, and opportunities of this emerging technology. 

Value alignment 

The most important attribute of a usage-based pricing model is that all pricing logic is anchored to a clear, measurable usage value metric. This represents how you will quantify how much of your service a customer uses and, ultimately, how much you will bill them for that use. 

Landing on the right value metric requires clean, accurate, auditable, and defensible data. If your product team is launching an AI product, chances are they’re already collecting, measuring, and translating the very data you’ll need for a usage model rollout. 

Usage-based pricing ensures customers pay in proportion to the value they derive, fostering a fair and transparent relationship. And your customers already understand this correlation—research indicates that IT buyers and consumers alike report a preference for usage-based charges for GenAI services.  

Ease of adoption

Lowering the initial cost barrier is crucial for businesses launching an AI offer. By eliminating hefty upfront fees, customers can start small, see the product in action, and consider if they want to sign up for a more robust package when the time is right. 

Usage pricing allows your customers to bite off smaller bits of a product, try it out, and quickly prove the value. 

Pricing agility

As customers adopt and grow more confident in your AI features or solutions, they’ll expect the nature of how they pay for their consumption of your product to change as well. With the right technology in place, you can not only collect data about how your customer is using your product, but also quickly test and pivot usage pricing metrics and models to scale right along with changing customer demands.

Cost management

Even though it is usually not advisable to price based on costs for intangible services such as AI, usage-based metrics can potentially help mitigate the associated high costs and lower profit margins.

Transparency and visibility

Customers are increasingly demanding a clear return on investment (ROI) and lower upfront risk. This is especially true for AI, where customers want a clear picture of what they’re using and how much value they’ll derive from your product. 

Usage pricing offers transparency and builds trust with customers by charging them for the resources they use and nothing more. With features like real-time usage visibility, customers can feel more in control, enabling them to track daily progress, anticipate overages, and view billing charges. This helps improve customer satisfaction and creates upsell opportunities for your sales team.

Predictability

While AI offers and usage-based pricing models alike are typically associated with variable costs based on actual usage, companies can introduce predictability by introducing some level of recurring commitment. When usage-based pricing is implemented in this way, as part of a hybrid business model, it can lead to higher year-over-year (YoY) growth. 

Additionally, with the right monetization platform in place, businesses can leverage usage forecasting to monitor behavior and predict expansion opportunities for high-usage customers.

Overcoming the challenges of AI monetization

While the shift to a usage-based model is strategically advantageous, it requires more than merely adjusting pricing levers. Successful AI monetization involves implementing robust strategies and technologies that enable accurate usage metering, value quantification, and streamlined billing and revenue recognition processes. 

This approach not only addresses the actual value delivered to customers but also accommodates a wide range of customer needs at a fair price, enhancing the resilience and adaptability of SaaS providers.

The road ahead

As the adoption of AI continues to grow across industries, usage-based pricing models will become increasingly pivotal in providing access to innovative and rapidly evolving technologies. This model offers a scalable, transparent, and cost-effective approach that benefits both providers and users, aligning with the dynamic nature of modern business practices. 

As you navigate the complexities of AI in the SaaS sector, embracing usage-based pricing is a strategic step towards fostering innovation, maximizing growth, and future-proofing monetization strategies.

To learn more about how to launch and scale a usage model for AI, check out our ultimate guide to usage-based pricing.