Across industries, artificial intelligence (AI) has emerged as a valuable tool for transforming customer experiences, improving operational efficiency and driving business growth. But no matter your company’s size, the cost of building and maintaining AI technologies can be a challenge.

One of the most important steps in adopting AI solutions is choosing the right pricing model. With such a wide variety of options out there, trying to select the right one — balancing your current needs, your future aspirations and your budget — can become quite the juggling act. Understanding the strengths and trade-offs of each of the main approaches to pricing is key to making a wise decision.

Let’s look at the six most common AI pricing models — license-based, consumption-based, subscription-based, freemium, revenue-shared and outcome-based — to help expedite your artificial intelligence cost estimation. Whether you’re just beginning your AI journey or refining your existing strategy and capabilities, these insights will help you manage affordability while maximizing the potential of AI tools to deliver impactful results.

AI Pricing Models: Which Is Best for Controlling Costs?

Each of the basic pricing models that AI vendors offer has its own strengths and limitations. Before you start your next AI project, take a closer look at how they stack up in terms of cost management.

1. License-Based: Predictable but Costly Upfront

License-based AI models charge a one-time fee for access to AI software over a set period. This approach offers budget predictability, as costs remain fixed throughout the license term. However, the high upfront investment can be prohibitive for smaller businesses or those needing flexibility. License-based pricing works best for organizations with well-defined, long-term AI requirements.

2. Consumption-Based: Flexible but Variable

Often referred to as pay-per-use, this type of AI model charges based on specific usage, such as API calls or data volumes processed. Consumption-based pricing offers scalability and flexibility, allowing businesses to adjust their AI systems usage as demand fluctuates. It’s ideal for companies with seasonal or variable needs; however, a need for a flexible approach to financial planning will be required. For businesses seeking real-time adaptability, or simply the freedom to experiment, this model can provide a balance between control and cost-efficiency.

3. Subscription-Based: Consistent but Rigid

With subscription-based pricing, businesses pay a recurring monthly or annual fee for continuous access to AI services. This model simplifies financial planning by offering consistent payments, making it attractive for organizations with steady AI usage. However, its fixed nature means you could be paying for capacity you don’t fully use during slower periods — or paying for users/seats that rarely use the tools. This can make a Software as a Service (SaaS) pricing model potentially inefficient.

4. Freemium: Low Risk but Potentially Expensive

Freemium models provide a low-risk entry point, offering basic AI capabilities for free. As businesses grow, they can unlock advanced features or scale up use by transitioning to a paid plan. While this is an accessible way to test AI solutions, costs can quickly escalate as needs expand. Freemium is ideal for companies that want to explore their AI options, but it requires careful monitoring to prevent unanticipated expenses. Additionally, advanced capabilities aren’t always offered with this model.

5. Revenue-Shared: Aligned Goals but Complex

Revenue-shared pricing ties vendor compensation to the financial outcomes the AI capabilities help generate. This reduces upfront costs and aligns the AI vendor’s success with your own, incentivizing them to work for your success. However, as AI-driven business operations scale up, attributing revenue directly to the AI solution can become complicated. And that potentially adds layers of complexity and fuzziness to your financial management.

6. Outcome-Based: Results-Focused but Hard to Define

Outcome-based pricing links payment to specific results, such as achieving predefined business objectives. While this minimizes financial risk by ensuring you only pay an AI vendor for measurable success, it can be challenging to define and agree on clear performance metrics. Projects may struggle to gain traction because of disagreements over how to measure success, making this model difficult to implement effectively.

Why Tokenization Makes Sense for AI Pricing

At Genesys, we believe AI pricing should be flexible and cost-efficient. That’s why we’ve adopted a consumption-based model powered by Genesys AI Experience tokens. Tokenization in AI is a way to track AI engagement in real time by allocating fixed units of measurement to usage costs. This can help businesses of all sizes allocate resources dynamically and efficiently. By paying only for the AI functionalities you actually use, tokenization offers a scalable, cost-efficient way to integrate AI into your operations.

Real-World Success: Cost-Effective AI Integration at Virgin Atlantic

Cost-effective AI adoption doesn’t have to begin with massive investments. Many companies find success by starting small and scaling strategically. For example, Virgin Atlantic began its AI journey with predictive routing from Genesys, an affordable solution that delivered quick returns by significantly improving customer service.

In addition to delivering faster resolutions by connecting customers with the most suitable agent, predictive routing improved employee engagement by aligning contact center agents’ strengths with the tasks they’re best equipped to handle. “Our agents now feel like they’re providing great service to the right person at the right time and are able to take ownership of the relationships with customers to truly build a rapport with them,” said Louise Phillips, VP of Customer Centres at Virgin Atlantic and Virgin Atlantic Holidays. “This has raised employee morale which in turn allows them to provide better service and feel proud of their hard work.”

Once the value of AI was proven, Virgin Atlantic committed to a longer-term plan to partner with Genesys to expand its AI capabilities.

The approach Virgin Atlantic took highlights the benefits of beginning with a targeted solution before scaling up. When evaluating any AI implementation, it’s essential to think beyond upfront costs.

Consider the total cost of ownership, which includes ongoing AI maintenance costs, scalability and — crucially — how quickly you might expect to see potential ROI. By investing in a use case that’s most likely to generate clear results for the business, you’ll be well-positioned to pursue further AI strategies. And a surprisingly fast ROI can help to justify further investment.

Start Small, Scale Wisely with AI

AI has the potential to transform customer experience and business operations, but managing its costs is a key component to unlocking its full value. By understanding the strengths and challenges of different AI license models you can choose a strategy that aligns with your goals and budget

Genesys AI Experience tokens offer a scalable approach that adapts to your unique needs. Whether you’re just starting out or ready to expand, this flexible model empowers businesses to innovate confidently while optimizing resources.

Download our eBook to learn more about how this model can help you achieve cost-effective AI integration and sustainable growth within and beyond the contact center.