Why You Need to Think Differently About Billing for AI Offers

For many years, companies across industries have relied heavily on fixed pricing models. These models, while straightforward, often fail to align with the diverse needs and consumption patterns of customers. 

A one-size-fits-all approach can lead to dissatisfaction, as customers may feel they are paying for services they don’t fully use or that their unique usage patterns are not adequately accommodated. The majority of companies are still using seat-based pricing for AI need to start moving to usage. According to a report from Zuora and BCG, IT buyers report a preference for usage-based pricing. 

By allowing customers to pay only for what they use this model ensures a fairer and more transparent billing process. Let’s see why traditional SaaS pricing might not be the best approach for AI. 

Why Should Companies with AI Capabilities Move to Usage Billing? 

The cost of AI software in 2024 varies widely, from free options to over $300,000. The price largely depends on whether an organization buys an off-the-shelf product or develops a custom solution in-house. An internally created AI system can have a total cost ranging from $6,000 up to more than $300,000 when factoring in initial development and ongoing deployment expenses.  

This wide cost range highlights the need for a more flexible pricing model that can accommodate varying levels of investment and usage. That is why the pricing model for AI products and services should be better suited to reflect the operational overhead and development costs. This isn’t possible with the traditional subscription model.

And That’s Why Cookie Cutter Pricing Doesn’t Work for AI capabilities 

As artificial intelligence becomes more integral to business systems and workflows, traditional per-seat pricing models are proving inadequate. With AI, the number of direct software users often belies the technology's enterprise-wide impacts. Meanwhile, seat licenses fail to capture AI's processing scale, complexity, and resource demands.

Specifically, an AI system may have just a few direct users, yet transform operations company-wide. In such cases, limiting AI costs to licenses for individual seats unfairly restricts pricing and under-captures AI's true value to the organization.

Another drawback of seat-based fees is that they do not reflect variables like the data volumes AI computes, the intricacy of its predictive modeling, and the cloud or computing infrastructure it requires. An AI system's functional breadth, depth, and power far outweigh considerations of how many employees log in.

Moreover, seat-based pricing is static, while AI involves dynamic costs around continuous training/upgrading and maintenance. For companies launching AI capabilities or products, per seat licensing can thus bottleneck revenue growth and scale.

As AI becomes more embedded into workflows, new pricing models are needed to align AI software costs with its business-wide benefits, functional footprint, and ongoing management demands. Given AI's expanding and fluid enterprise impacts, legacy per-seat approaches now seem antiquated.

So, what’s the alternative? 

What is Usage-Based Billing?

Usage-based billing, also known as consumption or metered billing, is a model that charges customers based on their actual usage of a product or service. This approach has gained significant traction in recent years, particularly in industries such as telecommunications, utilities, and software as a service (SaaS). 

According to a report by MarketsandMarkets, the global usage-based billing market is expected to grow from $4.4 billion in 2020 to $11.2 billion by 2025, at a CAGR of 20.6% during the forecast period. The benefits of usage-based billing are numerous, including increased transparency, improved customer satisfaction, and enhanced revenue management. 

Key Billing Features You Need for AI Monetization 

To effectively launch and monetize AI capabilities or products, your billing software should include the following features:

1. Usage-Based Billing: Track and bill based on the extent of service utilization, considering factors like computing power, data analysis depth, and real-time insights provided.
2. Customization: Offer personalized pricing and packaging that cater to the specific needs of diverse clients.
3. Agility: Adapt pricing strategies quickly in response to technological advancements, competitive pressures, and changing market demands.
4. Scalability: Ensure that billing operations can scale seamlessly with the growth of both the AI service and its user base.
5. Real-Time Data Processing: Provide real-time usage analytics, value metric tracking, and billing calculations to stay agile and responsive to changes in usage patterns.
6. Integration Capabilities: Easily integrate with other business systems (CRM, ERP, data analytics) for holistic management.

Usage-based billing is revolutionizing the billing space by offering a more flexible, efficient, and customer-centric approach. For companies that are developing or launching AI capabilities, adopting a usage model is not just about improving operational efficiency, it’s about aligning costs with value, enhancing customer satisfaction, and driving growth. As AI technology evolves, we can look forward to even more transformative changes in how AI services are priced and consumed.

To learn more about usage-based billing for AI, join us at Subscribed Live on June 26.