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.

The end of the zero-interest-rate period (ZIRP) has caused an awkward shift in the Minimum Viable Product (MVP) model, forcing the tech industry to redefine how quality is defined in an era when capital is low, the software market is saturated, and switching costs are lower than they’ve ever been.

Ever wondered if there’s more to software quality than just testing? The distinction between software testing and quality assurance (QA) might surprise you. While they are closely related and aim to ensure high-quality software, their roles and processes differ. Let’s evaluate the differences between QA and software testing and where they overlap.

When using MySQL and InnoDB you will inevitably run into lock timeouts sometime, somewhere. We have recently started seeing this with some of our Shopware 6 customers in their storefronts or worker queues, so I was reminded to go back to 2017 in our codebase when we put a fix in place. In our case, this happened on tables that were constantly written to from many different sources in the code base.

In the pharmaceutical industry, an effective supply chain is critical, not only for operations but also for ensuring the safety and quality of products going out to market. The complex nature of pharmaceutical supply chains, combined with their stringent regulatory requirements, makes risk management a top priority.

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.

Testing tools in AI and data automation have evolved to offer sophisticated features that ensure the quality of the end product and reduce its time to market. With reports suggesting 50% of manual testing being replaced by automated, these tools can help with various aspects of software testing, including unit testing, performance testing, and security testing. They can be integrated practically anywhere across the CI/CD pipeline for continuous testing and shift left testing.

In the ever-evolving landscape of software testing, the transition from manual to automated testing is a journey filled with misconceptions, challenges, and significant rewards. Recently, I had the privilege of hosting a conversation with two industry experts, David DiCocco and Alex Martins, who shared their personal experiences and insights on this topic.