The Importance of Aligning Price and Value for AI Monetization

As implementations of artificial intelligence move forward, digital businesses and product leaders must also evolve their approaches to pricing their AI products. The price charged must align with the value delivered to customers—while also providing reliable revenue. Now’s the time to carefully evaluate how to price your AI products and features.  

AI Pricing and Packaging Considerations 

AI is costly. Some software suppliers may develop their own large language models. Many will use third-party AI, agentic AI, and generative AI models, relying on existing LLMs, enriching and fine tuning them with domain specific data, then integrating them into dedicated applications. Both approaches carry significant expense.  

Because pricing and cost scenarios are difficult to predict in advance, technology companies are facing a new set of hurdles in order to sell profitable AI-based products. Software suppliers must evaluate a range of AI-specific pricing variables in order to evaluate the true cost of a product or feature and protect margins.  

Consider: 

• How is the AI solution delivered to the customer? Is it a new feature integrated into existing products (enrichment model), requiring price adjustments across the portfolio? Is it an optional add-on or standalone offering that can be purchased separately? Or is it a completely new AI product with its own pricing logic?  

• Does the product integrate third-party components (such as for analytics, reporting, or search)? If so, what are the associated procurement costs that must be passed along to the end-user? If not, what are the true and complete costs of developing and maintaining the AI offering? If a combination of both approaches, are all expenses accurately accounted for in pricing evaluations? 

• Does the product deploy a custom AI implementation with deep learning models and real-time inference? Or does it rely on a less expensive chatbot? 

• What are the expenses associated with the massive computing power, data processing, storage, and cloud costs associated with the AI product or feature? 

• If AI features replace human labor, how might the number of user licenses drop? 

• How does usage fluctuate? What are the usage patterns that provide insight into customers’ perceived value of the offering? What monetization models best meet their needs? 

Vendors looking to implement new business models must be prepared to operate in a data-driven way. Answers to questions like these must be factored into what to charge customers.  

Hurdles to Aligning Price and Value 

Pricing remains a challenge. Only about one-third (36%) of software suppliers are confident that their pricing is “totally aligned” with the value delivered to customers.  

Without insights into users and their priorities—and with siloed systems—aligning price and value will remain problematic. These pricing issues are difficult enough for any software company. When adding AI to the mix, successful pricing decisions become even more important in order to ensure profitability. 

Source: Revenera Monetization Monitor 2025 Outlook: Software Models and Strategies  

As usage patterns change, it’s essential that the price charged and the value (as perceived by the user) align. A one-size-fits-all solution is rarely helpful. Vendors need flexible and adaptable pricing strategies to fully realize the monetization potential of their applications.  

Alternative Business Models for Pricing AI 

How you charge often matters more than how much you charge. This is true across the board for software monetization—and especially so for AI. Some legacy monetization models, such as perpetual (in which a user pays once for unlimited use of software) and even subscription (in which a user pays monthly or yearly for use of software) offer predictable revenue for a software supplier, but frequently fail to reflect actual usage patterns.  

Alternative business models for monetizing AI are gaining ground: 

• Usage-based monetization models: Usage-based pricing for AI offers less revenue predictability for the software supplier, but more accurately reflects how each customer is using the product. Variants range from fully flexible pay-as-you-go structures to usage-based subscriptions with more predictable revenues. These models also align well with the cost structures of GenAI and give users greater flexibility, reflecting costs associated with frequency (such as how often searches are performed) and type of usage (allowing for accurate billing based on the computing power or cloud storage requirements associated with usage, for example).  

• Outcome-based models: This approach may be seen as an advanced form of usage-based pricing, aligning price with business outcomes, rather than just usage. Users pay not only for access, but also for measurable results defined by key performance indicators. In an AI-powered scenario for customer service, for example, this might include the KPI of the number of automatically resolved support tickets. To ensure provider profitability, the per-outcome pricing is typically higher with this model than may be the case with other usage-based approaches.  

• Hybrid monetization: Since cloud and AI infrastructure generally rely on variable cost structures, many providers are adopting hybrid models. A common approach combines subscriptions with elastic access—prepaid usage components. The subscription covers standard usage, while prepaid credits (tokens) may be applied for peak loads, premium features, or flexible access to additional services. This hybrid approach ensures that the provider has a recurring revenue stream, while also ensuring that customers enjoy flexible access to a provider’s offerings—and can even explore the varied products and features available in a software supplier’s portfolio. 

New Technology, New Approaches to Monetization 

Business growth requires that customers’ needs are met. When it comes to AI, pricing products and features in a manner that’s compatible with users’ needs is essential. Data and analysis about expenses and usage trends are essential in order to successfully align price and value. In the long run, it’s not just what AI can do that matters, but how smartly it’s monetized.