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

Why consumption 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 consumption-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 consumption-based pricing model is that all pricing logic is anchored to a clear, measurable consumption 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 consumption model rollout. 

Consumption-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 considering GenAI solutions report a preference for usage-based pricing. 

Ease of adoption

Lowering the initial cost barrier is crucial for businesses considering AI integration. 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. 

Consumption 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 quickly test and pivot consumption pricing and packaging to scale with changing customer demands.

Cost management

Consumption pricing provides better control over expenses by linking customer usage data directly to costs. This visibility allows for more accurate budgeting and forecasting, enabling your company to optimize their AI investment based on performance metrics and cost considerations.


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. 

Consumption pricing offers transparency and builds trust with customers by charging them for the resources they use and nothing more. 

Many companies choose to meter usage data using a mediation tool, which can help minimize errors and customer disputes. With real-time traceability of every single usage record, from data ingestion to invoice.


Customers value real-time consumption visibility, enabling them to track daily progress, anticipate overages, and view billing charges. The ability to push out threshold notifications allows customers to monitor their spending and increases overall customer satisfaction. Usage forecasting can enable businesses to monitor behavior and predict expansion opportunities for high-consumption customers.


While AI offers and consumption-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 consumption-based pricing is implemented in this way, as part of a hybrid business model, it can lead to higher year-over-year (YoY) growth

Overcoming the challenges of AI monetization

While the shift to a consumption-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, consumption-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 consumption-based pricing is a strategic step towards fostering innovation, maximizing growth, and future-proofing monetization strategies.


Keep Learning

The Ultimate Guide to Monthly Recurring Revenue (MRR)
What ASC 606 means for revenue recognition
Understanding material weakness in internal control for finance
SaaS pricing models: A comprehensive monetization guide