Application programming interfaces (APIs) power numerous applications and services. In today’s fast-paced digital landscape, seamlessly accessing digital services across devices and platforms have become the defining trait of modern software systems. APIs also play an important role in defining this connectivity. When you begin developing an API, you may not pay much attention to API documentation or the development tools you employ.

Enterprises are continually seeking innovative ways to optimize their pricing models and drive revenue growth. Usage-based billing, where customers pay based on their actual consumption, has emerged as a compelling strategy. Unlike traditional flat-fee models, usage-based billing aligns more closely with customer needs and behaviors, offering flexibility and scalability that are crucial in a competitive market.

Artificial Intelligence (AI) technology has been transforming industries and our day-to-day lives alike. Its undeniable impact has led to significant effort and investment into making AI more accessible to everyone, everywhere. Open-source AI technology and AI APIs are two examples of our commitment to AI democratization. AI APIs democratize AI by providing access to pre-trained AI models, even for developers without extensive machine learning expertise.

You’ve built an incredible AI API and are ready to release this functionality to your users. The issue is that you’re not sure exactly how to monetize it. Generally, monetizing APIs is challenging at scale, but monetizing AI APIs can be even more difficult. Some AI APIs may be charged on a “per API call” basis, but many AI APIs require charging users for input and output tokens used within an API call. Others may charge per unique user or API key.

Regarding charging for API usage, we usually gravitate towards charging per API call. While this can work for many use cases, it’s not optimal for everyone. This is where choosing the right metric to bill upon and finding a platform that supports it is crucial. In this blog, we will talk about choosing the right metric to bill upon and how to implement it in Moesif. Let’s begin by digging into what a billing metric is and how to decide which one is best to use with your AI APIs and services.

Machine learning APIs provide developers with powerful tools to integrate complex algorithms and models into applications without building them from scratch. These APIs simplify the development process by offering pre-trained models and standardized methods for different tasks. These include image recognition, natural language processing, and predictive analytics. This accessibility democratizes machine learning so that developers of varying expertise can leverage cutting-edge technology efficiently.

For many Gen AI-based applications, usage-based billing is crucial. It is especially important if you are using third-party models that charge you for tokens used. You want to ensure that your cost of tokens used is covered by your customers, in addition to charging for your own value add. In this tutorial, we will build a quick Chat Bot API using OpenAI’s ChatGPT, and then use Moesif and Stripe to meter the number of tokens used and report the usage to Stripe to be charged.