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.

The open-source philosophy and principles have always been about the democratization of knowledge—giving everyone the opportunity to access, learn, modify, and share software freely. As we see AI technologies seemingly taking over our day-to-day lives, we need those benefits in AI as well. Open-source AI inherits the open-source principles. It fosters transparency and collaboration, allowing us to build robust and secure AI systems.

Product analytics helps you understand how users interact with your product, offering insights that drive growth and improve user experience. In this guide, you’ll learn about essential metrics, strategies, and tools for effective product analytics. By leveraging these insights, businesses can make informed decisions, optimize product features, and enhance overall user satisfaction.

Not long ago, the thought of Artificial intelligence (AI) products becoming mainstream seemed far off. Fast forward to the widespread adoption of ChatGPT, Gemini, and other large-scale apps, and it’s easy to see that the world has changed drastically. AI isn’t just the future anymore – it’s the present, and every business is hopping on the bandwagon. From chatbots streamlining customer service to algorithms predicting market trends, AI is reshaping how businesses operate.