AI is transforming industries, and with it, the way we think about pricing models must evolve. Traditionally, AI solutions have been priced based on consumption—charging customers for the amount of data processed or API calls made. However, as AI applications become more sophisticated, companies are exploring new pricing strategies that better align with customer value and business outcomes. In this blog, we’ll explore how AI pricing models are evolving from consumption-based approaches to hybrid models and the emerging concept of generative pricing.

The Traditional Consumption-Based Model

Consumption-based pricing has long been the go-to model for AI solutions. It’s simple: customers pay for what they use—whether it’s input and output tokens, API calls, data processing, or compute time. This model provides flexibility and scalability, making it attractive for early-stage AI products. It is closely associated with costs.

However, as AI applications become more integral to business operations, consumption-based pricing can fall short in reflecting the true value delivered. A company may process fewer API calls but derive immense value from the insights generated by an AI tool. This disconnect between usage and value has driven the search for more value-centric models.

Example: Salesforce moves to usage based pricing

Salesforce has historically used consumption-based pricing for its AI-powered tools like Einstein Analytics. Customers pay based on their usage of features such as predictive analytics or data processing. While this model works for some use cases, it usually doesn’t capture the full value delivered to businesses using these tools.

The Rise of Value-Based Pricing

Value-based pricing is gaining traction in the AI world because it aligns prices with the outcomes customers achieve. Instead of charging based on usage alone, companies using value-based pricing set prices based on how much value their product delivers to customers—whether it's increased efficiency, cost savings, or revenue growth.

At Ibbaka, we’ve seen firsthand how companies can leverage value-based pricing to better reflect the true worth of their offerings. By building robust value models that quantify customer outcomes, businesses can ensure their pricing reflects the economic value they create.

Example: OpenAI experimenting with value-based models

In our previous blog on Pricing Approaches for Generative AI, we explored how companies like OpenAI have started experimenting with value-based models for their generative AI tools. These companies charge based on the tangible business outcomes their products deliver—such as time saved through automation or improved decision-making accuracy—rather than just usage metrics.

Hybrid Pricing Models: Flexibility Meets Value

Many companies are now adopting hybrid pricing models that combine elements of both consumption-based and value-based approaches. These models offer flexibility by allowing businesses to charge based on both usage and customer outcomes.

Hybrid models are especially useful in B2B SaaS environments where different customers may derive varying levels of value from a product depending on their specific use case. For example, one customer might need extensive data processing capabilities (favoring consumption-based pricing), while another might prioritize strategic insights (favoring value-based pricing).

Example: Outcomes become part of hybrid pricing

In our post on Generative Pricing, we discussed how generative AI applications often require hybrid models that combine multiple metrics—such as token usage (consumption) and user outcomes (value). This approach allows businesses to capture both the operational costs of running AI systems and the economic impact they deliver to customers.

Generative Pricing: A New Frontier

As generative AI applications grow in complexity, traditional pricing models may no longer suffice. Enter generative pricing—a dynamic approach that adapts in real time based on how an application is configured and used.

Generative pricing is particularly well-suited for composable applications that can be tailored to meet specific customer needs in real time. This model allows prices to adjust dynamically based on configuration changes, ensuring that customers only pay for what they need at any given moment.

Key Characteristics of Generative Pricing

• Responsive: Prices adapt quickly to changing configurations.

• Explainable: Transparency is key—customers need to understand how prices are determined.

• Value-Based: Prices reflect the economic value delivered by the application.

How This Works Together

1. Real-Time Configuration → Responsive Pricing:

• As configurations change in real time (e.g., adding/removing features), prices adjust accordingly. This ensures that customers only pay for what they use at any given moment.

2. Transparent Communication → Explainable Pricing:

• Once prices adjust responsively, it’s critical that customers understand why and how these changes occurred. Transparency builds trust and helps customers make informed decisions about their configurations.

3. Customer Outcomes → Value-Based Pricing:

• Finally, the price should reflect the actual value delivered by the AI solution—whether it’s cost savings, increased efficiency, or revenue growth. This ensures that customers feel they are paying a fair price for the benefits they receive.

In our blog The Path to Generative Pricing, we outlined a five-step approach to implementing generative pricing for B2B SaaS companies. This approach emphasizes building a formal value model, mapping features to value drivers, and designing dynamic pricing that changes in real time based on customer needs.

Choosing the Right Model for Your AI Solution

So how do you choose the right pricing model for your AI product? The answer depends on several factors:

• The nature of your product (e.g., API-driven vs. composable applications)

• Your target market (e.g., small to medium-sized businesses vs. enterprise clients)

• Customer expectations (e.g., flexibility vs. predictability)

For many companies, a hybrid approach that combines consumption-based elements with value-driven metrics offers the best balance between flexibility and customer satisfaction.

Insight

Start by building a robust value model that quantifies how your product delivers measurable outcomes for customers. Then combine consumption metrics with outcome-based metrics to create a flexible hybrid model that caters to different customer segments.

Conclusion

As AI continues to evolve, so too must our approaches to pricing these innovative solutions. While consumption-based models have served us well in the past, hybrid approaches that blend usage with outcome-driven metrics offer greater flexibility and alignment with customer needs. Looking forward, generative pricing represents the future of AI monetization—offering dynamic, responsive, and transparent pricing tailored to real-time configurations.

By adopting these advanced pricing strategies, businesses can not only capture more value but also enhance customer satisfaction by aligning costs with outcomes.

Contact Ibbaka for guidance on how to improve your pricing and your pricing page.