Arpita Maity at Genesys explores 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.
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.
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 organisations 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 organisations 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, incentivising 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 minimises 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.
This blog post has been re-published by kind permission of Genesys – View the Original Article
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About Genesys
Every year, Genesys orchestrates billions of remarkable customer experiences for organisations in more than 100 countries. Through the power of our cloud, digital and AI technologies, organisations can realise Experience as a Service, our vision for empathetic customer experiences at scale. With Genesys, organisations have the power to deliver proactive, predictive, and hyper personalised experiences to deepen their customer connection across every marketing, sales, and service moment on any channel, while also improving employee productivity and engagement. Find out more about Genesys
Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.
Author: Genesys
Reviewed by: Megan Jones
Published On: 9th Jan 2025
Read more about - Guest Blogs, Genesys
