Generative and agentic artificial intelligence (AI) are revolutionizing pricing models in technology, media, and telecommunications industries. As AI technologies become more sophisticated, companies need to think beyond the tech itself and more about how customers experience the AI products they develop and the tangible outcomes they drive.
Output isn't just a product or service anymore, it's impact. Rather than buying access to a platform; customers are buying better decisions, higher efficiency, or predictive power. Especially for enterprise customers, the value can be massive.
That's why companies are exploring innovative pricing strategies that better align with customer value and business outcomes. It's a bold and logical evolution, and one that's going to reshape how we think about pricing, especially in AI-heavy industries.
The challenge with user-based pricing
If you're still thinking in terms of cost-plus pricing for AI, you're missing the forest for the trees. The cost to run a model says nothing about the value it's delivering. Marginal costs are low, and the costs to run AI models are dropping every day, value is variable, and innovation can’t be priced with a spreadsheet. It's the most basic (and increasingly outdated) model.
Many companies default to charging per API call, token, or seat. It’s predictable and maps easily to cloud infrastructure costs. However, as AI technologies automate tasks and reduce the need for human intervention, the number of users required to operate software decreases.
This reduction means that user-based pricing no longer accurately reflects the value provided by AI solutions. Companies need to shift toward pricing models that better capture the economic benefits and efficiencies delivered by AI.
Value-based pricing as a long-term strategy
Value-based pricing is gaining traction in the AI world because it aligns prices with the outcomes customer achieves. It's a more advanced approach because it demands clarity on metrics and a deep understanding of customer outcomes. But it also unlocks better alignment, stronger differentiation, and higher willingness to pay.
A telco deploying AI to reduce dropped calls or optimize bandwidth isn’t just using tech, they’re driving specific business outcomes. A media platform using AI to surface hyper-relevant content isn’t just “serving recommendations”, they're increasing session length, reducing churn, maybe even boosting ad ROI. That’s measurable impact. And that’s where value-based pricing shines.
Example of value-based pricing for AIOpenAI’s current pricing for ChatGPT and API services is still usage-based at its core, e.g., priced per 1,000 tokens, API call, or feature tier (like ChatGPT Plus at $20/month). However, these tiers reflect performance and capabilities, which is an early step toward value differentiation. OpenAI has also started offering bespoke enterprise packages that include:
These are priced individually, which strongly suggests they're being scoped based on expected business outcomes or value delivered, not just raw usage. It’s not public what pricing model is used, but such custom deals are often quasi value-based in practice. |
Value-based models reflect a bigger truth:
AI becomes no longer just a tool but an enabler of transformation. And when something can drive that kind of change, pricing by usage alone starts to feel inadequate.
Think of pricing AI like a ladder: cost-plus is the bottom rung. Usage gets you a bit higher. But to really capture and deliver the transformative value AI enables you need to climb toward value- and outcomes-based models. That’s where pricing becomes a growth lever, not just a revenue mechanism.
Value-based pricing is also way more defensible in competitive markets. If your AI consistently delivers higher ROI than a competitor’s, you have a strong argument to charge more, even if you’re selling the “same” type of product on the surface. It shifts the pricing conversation from cost to worth.
What makes this especially interesting with OpenAI is they sit at the foundation layer. They're powering other companies’ outcomes. So, if they go value-based, it sets a precedent all the way up the chain. Imagine everyone, from app builders to resellers, anchoring price on the value passed downstream. It could trigger a whole rethinking of the software economy.
Is value-based pricing a silver bullet?
Value-based pricing creates a built-in incentive for alignment: you succeed when your customers succeed. That can be incredibly powerful, especially in AI where adoption is still gated by trust and explainability.
But it’s not without friction. The key challenge with value-based pricing is that it demands mutual clarity. The customer needs to understand exactly how value is measured, and the provider needs access to enough data to prove it. That’s a high bar. You can’t just say “you’ll make more money”, you have to show the causal link. It’s like a pricing model wrapped inside a trust exercise.
It also demands more maturity from sellers. You have to deeply understand your customer’s KPIs, track performance continuously, and sometimes co-create success metrics. It's consultative, not transactional. Which means sales and pricing teams have to be much more aligned with product and analytics than in traditional models.
Hybrid pricing models
Hybrid pricing models combine elements of consumption-based and value-based pricing, offering flexibility while capturing customer value. These models allow companies to charge a base fee for access to AI tools, with additional charges based on the value delivered, ensuring pricing reflects both usage and the economic benefits realized by customers.
For some companies, hybrid models will be the sweet spot, especially where customers are still figuring out the true value of AI. Hybrid models allow you to hedge your bets between predictable, recurring revenue and upside potential.
Hybrid opens the door to gradual trust building. New customers can start with usage-based pricing and then evolve into deeper value-based tiers as they see the ROI unfold. You’re not asking them to commit to a black-box promise from day one. The base fee gives customers confidence. They know what they’re going to be paying, which feels familiar, like a subscription or licensing model. Then the variable layer tied to value or performance lets the provider participate in the growth their AI is fueling.
Unlock the full value of AI with Simon-Kucher
At Simon-Kucher, our industry experts are at the forefront of helping companies navigate the complexities of pricing in the age of AI. Our proven expertise in value-based pricing enables us to help clients shift from usage- or cost-plus models to strategies grounded in customer-perceived value, ensuring optimal monetization.
Whether you’re launching a generative AI product, enhancing existing offerings with intelligent features, or entering new markets, we help you align pricing with differentiated outcomes, build scalable revenue models, and accelerate growth.
Contact our team.
