Every slowdown in your application can be traced to specific components like a database or an API, and quickly identifying the source aids the troubleshooting process. But when an API is underperforming, it may be difficult to tell whether the issue is with the API logic itself or an external service like a database that it interacts with before sending responses.

Modern application development and IT infrastructure depend heavily on the use of APIs (Application Programming Interfaces) and API Management. APIs offer a standard set of rules for systems and applications to connect and interact. Whether you are integrating a new application into an IT infrastructure or connecting the modular components of a microservices-based application, APIs are one of the fastest and most cost-effective ways to establish those connections.

Interoperability is one of the main reasons to reverse engineer APIs. Unlike a few decades ago, when software could exist in isolation, nowadays, a program runs on a complicated operating system and involves communication with several libraries created by different people. Reverse engineering APIs for interoperability involves API integration and learning new ways in which programs can exchange and use information.

Since it was created by Facebook in 2012 and made publicly available in 2015, GarphQL has changed everything about how we fetch data from servers for our front-end apps. Most front-end clients typically use REST APIs to retrieve data from the server, this includes mobile apps for platforms like Android, iOS, and Flutter, as well as JavaScript frameworks like React, Angular, Vue, and Next. A huge advantage of GraphQL is that it enables front-end clients to request only the API they require.

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.

DreamFactory's custom SQL endpoints let you create APIs that are tailored to your exact needs, making your data more flexible and powerful. In this article, we will show you how to set up these endpoints, provide practical examples, and explain how to connect endpoints to your external systems.

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.

Far from being a mere byproduct, data is a hidden gem for any organization. While it's often seen as a technical hurdle, data is actually a powerful business tool. To stay ahead of the curve, companies need to transform into information-driven powerhouses that leverage data as the bedrock of strategic decisions. By recognizing the value of their data and fostering a culture of open information exchange, organizations can unlock a wealth of potential for improvement.

Bitbucket is a robust tool for source code management and continuous integration/continuous deployment (CI/CD). It offers flexibility in setting up pipelines, but there are limitations, particularly around root privileges and mounting volumes. This blog explores these limitations and provides detailed solutions using Docker-in-Docker (DinD) and Linux shell scripts.

This guest blog was written by Matt Turner from Moesif. Moesif provides insights and tooling for API product owners to activate, understand, and monetize customers.