Understanding Tokenomics: The Key to Profitable AI Products

Curious how much an LLM would cost your organization? Explore our dynamic token cost model tool and calculate it for yourself.

Benefits of a Strong Tokenomics Model

• Clarity: Aligns product design with budget realities.
• Control: Informs architectural decisions, like when to cache, route, or compress prompts.
• Confidence: Enables data-driven decisions on when and how to monetize.

AI Isn’t Just a Feature, It’s a Cost Driver

Unlike traditional SaaS, where usage costs are relatively fixed, AI introduces variable unit economics. Without tokenomics modeling, teams risk building amazing features that are financially unsustainable. With it, they gain a clear path to building not just smarter products but successful and sustainable businesses.

Reducing Token Usage in GenAI Applications

When building generative AI applications, the first step is defining what your application needs to do and what a good answer looks like. You need a clear vision of the inputs you expect the LLM to process, and of what kind of output from that LLM you would consider good. Moreover, it's highly recommended that you write evaluations to turn these expectations into executable tests.

Once that is clear, the next step is to build a robust prompt. You should describe in detail what the LLM should do and what the expected output is. Use as many words as you need to, and include several examples of inputs and outputs. The goal of this step is to get the outputs you want out of your generative AI application, which you can test using evaluations.

Once you're satisfied with the outputs, it's time to refine that large and robust prompt to its most concise and effective version. Input tokens cost money, and output tokens cost money. This step involves finding the smallest viable prompt that produces results comparable to those of a larger prompt. Here is where evaluations become critical, since they allow you to measure objectively how good a prompt is by assessing the responses based on known test cases.

After the prompt has been stripped down of everything non-essential and you have the smallest viable prompt, you can start applying other techniques such as Batch Inference and Prompt Caching. Batch Inference enables asynchronous inference, with delays measured in minutes or hours, while reducing token costs by 50% on Amazon Bedrock. Prompt Caching offers even higher cost reductions, but only for prompts where the initial portion doesn't change between invocations. These techniques don't apply to all use cases, but they're always important to consider.

How Caylent Can Help

Tokenomics is a crucial but often overlooked aspect of developing profitable AI products. By understanding how token consumption impacts costs, scalability, and monetization, organizations can ensure their generative AI applications are both innovative and financially sustainable. At Caylent, we specialize in helping teams navigate these complexities, providing the expertise and tools needed to build dynamic token cost models, optimize usage, and make data-driven decisions that drive profitability.

If you're looking to scale your generative AI products and accelerate your AI initiatives, Caylent is here to help. Our approach focuses on amplifying the power of your data, ensuring that you can drive innovation while maintaining efficiency across your operations. Reach out to us today to learn how we can help you turn your AI ambitions into a scalable and impactful reality.