An Intellyx Brain Blog for Amberflo

Everyone’s rushing to deliver generative AI based applications and products to market and cash in on the latest trend or gain competitive advantage.

At the same time it’s not clear that everyone understands the right way to set prices to ensure a return on investment.

If you are rolling out a generative AI-based application, incorporating AI into an existing application, or creating a new product that incorporates AI, taking a few minutes to develop the right pricing model will be an essential part of determining your success.

The pricing model for AI is important not only for the revenue it brings, but also because it has to be something customers easily understand. Finally, if your pricing model isn’t competitive, your customers will go looking for alternatives. This is a tricky balance to achieve.

Setting up usage based AI pricing

First, configure usage metering  for the gen AI tool you are using, as explained in the previous blog in the series. For an LLM-based tool, this means capturing the number of words sent in the input prompt and the number of words received in the output response, and calculating the backend price charged by the model provider according to the per-token rates.  

Popular generative AI tools such as Chat GPT, Anthropic, Google Gemini, Cohere, and Mistral charge on the aggregate token (i.e. word) level over a period of time, with varying price tiers depending on the quality of service you select or on an API call rate limit.  

So you first need to analyze and understand the pricing model for the gen AI tool you’re using, establish the metering, take into account any variations imposed by the particular AI tool, and structure your pricing model accordingly.

It’s also important to figure out the right margin to add, if any, to cover your costs and generate revenue.  

If the application is internal to your organization, there’s probably no need to include margin – especially if you are just passing along or dividing up the cost across different departments.  

However if you are reselling the service and adding value to it, such as security scanning for the prompts and responses or training the LLMs for industry specific needs, it is standard practice to  to add a reasonable margin to the gen AI usage fee to account for the additional value-add and generate a reasonable profit.  

Billing should be transparent, understandable, and flexible enough to adapt to the customer’s current payment processes, if possible.

Finally, set up an invoicing process to submit bills to your customers and track payments against them.

Subscription based pricing models are dying out

Many current and traditional software products rely on perpetual licenses or subscription support pricing models.

However, as products move to the cloud, which basically rents capacity across a pooled infrastructure, pricing models are changing to keep pace.

Cloud infrastructure servers fail continuously and other computers take over application workloads dynamically. Loads are automatically scaled up and down across multiple servers. This makes it very difficult to measure or enforce a perpetual or subscription based license, which typically counts servers or cores.

In the cloud you instead measure how a managed service such as compute, storage, or messaging consumes resources such as CPU, memory, disk, or network capacity and charge for that.

And of course ML and AI applications are among those primarily based in the cloud. So the marriage of AI and usage-based pricing is understandable.

Summarizing gen AI usage based pricing

First, analyze meter data to understand usage patterns and identify suitable billable metrics.

After you collect and identify billable metrics, you can roll them into products for sale as product items composing usage-based pricing models. When usage starts, tally, rate, and invoice the billable metrics.

The right pricing model is all the more important for AI-based products, since there’s so much competition and because they consume so many resources to process the prompts and deliver the results.

For example, recording a transcript and sending it to Anthropic for a summary has a certain cost basis. Understanding the cost basis is critical to determining the ROI of such a gen AI product.

Such flexibility in the pricing model is tricky to figure out — which is why platforms for building and managing usage based pricing such as Amberflo are helpful.

The Intellyx Take

AI and ML-based applications typically run in the cloud, or depend on applications running in the cloud for sufficient elastic capacity.

Calculating the ROI and collecting the right metrics for your AI-based applications can be tricky but it is nonetheless foundational for establishing the right pricing, billing, and invoicing practices.

Quickly and correctly setting and updating the pricing model for your gen AI based app or product is easier and faster if you use a dedicated AI monetization platform such as Amberflo.

Amberflo has predefined metrics, billing, and invoicing capabilities for the leading LLMs and AI chat bots. This really helps you figure out the right pricing or cost tracking strategy – whether it’s internal to your organization or for sale to external customers.

Copyright © Intellyx B.V. Intellyx is editorially responsible for this document. No AI bots were used to write this content. At the time of writing, Amberflo is an Intellyx client.