Today, we are announcing the launch of Amberflo AI Monetization Platform, an integrated set of product offerings that enable businesses to successfully monetize their next generation AI/ML and Usage-Based products and services.
While Generative AI models and AI/ML infrastructure providers are making their models accessible to developers via APIs, there are several critical monetization challenges that must be addressed to ensure the successful and profitable business outcome.
The challenge of AI monetization
AI/ML models and infrastructure are delivered on a usage basis. The provider charges for the use of the model and infrastructure on a metered usage model.
When businesses incorporate these models into their applications, they have to meter and track the underlying AI/ML infrastructure being used at a per user or customer level.
For example, several Generative AI models charge for metered usage measured in custom currency such as tokens. The user’s input and output interactions with the model are usage tracked or metered, and then rated and charged in the form of tokens consumed. The input and output tokens each have a different cost dimension. Similarly, other forms of AI/ML tools, models, and services charge based on different billable units, albeit, all are metered and charged on usage basis.
We can only expect AI/ML models to grow and evolve in variety, complexity and capability.
The challenge of effectively monetizing these services is first being able to successfully meter and track usage and costs, across different billable metrics, model versions, custom currencies (e.g. tokens), and more, while delivering scalable customer-facing pricing plans that both cover costs and account for value added margins. This is a heavy lift, requiring complicated workflows that manage and track ingestion, aggregation, rating, invoicing and billing on a realtime and consistently accurate basis.
Effective AI Monetization - a 3 legged stool of Metering, Quoting, and Billing
In a usage-based system, where usage and charges need to be computed in realtime based on actual use, the metering service is the platform layer that provides a common data structure (or usage graph) that forms the basis of integration and interoperability across the entire lifecycle of monetization from quoting, metering, to billing. Together, they serve as the integrated platform for monetizing any form of AI/ML and usage-based products and services.
Metering for AI/ML and Usage-Based services tracks all aspects of service consumption. Metering is used to associate resource consumption at a particular time to a specific user, customer, or entity. It answers the question (consistently, accurately, and in realtime) - what is being consumed by whom, where, how much and for how long? For more information about metering and how it differs from other events based systems, see this article.
To truly obtain a complete picture of the revenue and costs, each usage request and response (e.g. prompt and response) needs to be metered. This allows for tracking both internal costs incurred and the revenue generated from that same usage. Without a solid, accurate, and real-time metering foundation tracking each request as it takes place, it is impossible to deliver a pricing and billing solution for usage-based application and services.
To read more about Amberflo Metering Cloud, click here.
Quoting for AI/ML and Usage-Based services requires anticipating usage over the term of a proposed contract. Quoting is also referred to as Configure, Price, and Quote (CPQ)
To do this the quoting (or CPQ) tool in realtime needs to interoperate with realtime metering and billing system to be able to compute term charges based on usage and discount scenarios. The anticipated usage is plugged into this pricing plan to generate a quote. And with built-in and configurable approval workflows, once signed, the quote takes the form of a Commitment, that the underlying metering and billing system can automatically operationalize.
Quoting adds predictability to the usage-based model and thereby, accelerating the deal close process.
To read more about Amberflo Quoting Cloud, click here.
Billing for AI/ML and Usage-Based services, while working interoperably with metering and quoting systems, serves to operationalize the quote.
It takes the customer contract and generates on-demand, metered invoicing and billing based on the entitlement and terms. If a custom or private quote or offer was put in place, the billing system automatically translates it into a pricing plan and pulls the usage tracking from metering.
Unlike traditional billing systems, the billing system for usage-based pricing provides both built-in usage cost, revenue analytics, and backoffice revenue recognition integrations.
Customers and users are able to see up-to-date invoice balance, draw down and credit amounts at any point in a billing cycle backed by a full audit trail to guarantee accuracy and transparency in billing.
To read more about Amberflo Billing Cloud, click here.
Generative AI is a transformative technology that will soon become standard fare in the next generation of solutions requiring businesses to rethink their pricing and billing strategies. Companies that lag behind in gaining familiarity with this new paradigm will be disrupted by smaller, newer outfits that are successful in leveraging generative AI to make legacy solutions (or even whole categories) more efficient, personalized, and capable.
While there may be some early rewards for companies that are quick to deliver the first solutions in this category, the lasting winners will be those that are successful in defining and implementing viable and scalable monetization models for AI/ML initiatives.