Systems | Development | Analytics | API | Testing

Understanding API design is crucial for modern software development. Two popular approaches are GraphQL and REST. These technologies shape how data is exchanged and used over the internet, but they differ significantly in how they allow clients to interact with the server and retrieve data. Let's compare them in this article!

Storing information in a relational database and sharing it via a RESTful API is a very common scenario these days. Depending on your RDBMS (relational database management system), this may take a lot of development effort. One solution that attempts to simplify the process is the PostgREST web server add-on for PostgreSQL. If your organization uses the PostgreSQL database, PostgREST quickly adds basic CRUD (create, read, update, delete) functions in an easy-to-use RESTful HTTP interface.

Check out Ken Ahrens and Scott Moore as they discuss some blockers of developer productivity when building in Kubernetes, and how removing environment and data challenges can reduce toil and frustration! You can catch the full podcast on Scott’s page here: Scott Moore: Hey everybody out there in internet meme land. It’s time to hide your kids and hide your wife because it’s time for the SMC Journal podcast. Some of you will get that joke. Others will not.

See a solution that bridges the gap between gRPC services and RESTful HTTP APIs, and is enhanced with Apigee’s enterprise API management platform.

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.