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.

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.

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.

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.

Upon close inspection of the growing adoption of artificial intelligence across different industries, it becomes apparent that a lot it rests on the shoulders of API-powered systems. A survey by McKinsey found that 56% of respondents reported AI adoption in at least one function within their organizations, with significant increase in the use of AI APIs for tasks like natural language processing and computer vision.

API governance sets the rules and practices to manage APIs efficiently, ensuring they remain discoverable, consistent, and secure. It’s crucial for businesses aiming to control API sprawl and enhance collaboration. This guide explores API governance, its role in effective API management, its benefits, and best practices for successful implementation.

An API model acts as a blueprint for building scalable and maintainable software interfaces. In this article, you’ll discover how to design, implement, and validate a robust API model, including the use of AI models. We’ll cover key concepts like API modeling, essential components, user identification, and best practices.