Three API approaches for cloud-native technologies to consider.

by | Sep 22, 2023

There are a variety of possibilities when designing an API strategy for the newest technology, which may be categorised into three main categories.

A modernization plan is the first step, which entails decomposing monolithic systems into services, adopting cloud native practises, and, of course, integrating with mainframe-based mission-critical applications. Secured APIs are developed and kept up for this technique. The idea of adding features and functionality to APIs first and then opportunistically presenting that capability to the user interface is known as headless architecture. This is a second area to build an API strategy. Microservices-based architecture at the granular level, or one that relies solely on APIs, is created to simplify integration and automation.

The third aspect of API strategy to concentrate on is new technologies, including choosing technology stacks and integrating them with emerging technologies like AI, serverless computing, and edge computing. API ecosystems are created to draw clients and partners who contribute to and use public APIs. All API strategies must, above all, incorporate API administration and a security perspective.

The whole lifecycle capability for API design, testing, and security should be included in API management solutions. For the creation, testing, publication, and consumption of APIs, businesses may use DevOps and complete lifecycle management thanks to additional capabilities like analytics, business intelligence, and an API portal.

The following are a few further instances of contemporary cutting-edge technologies how it is being understood and utilized them may be a component of an API strategy

  • Integration of DevOps is the first. There are several open source and paid alternatives available for DevOps automation. Tooling for continuous delivery and integration are important components.
  • The other highly important area is data and AI technology, which has hundreds of possibilities for every phase of the AI development lifecycle, including data collection and management, data analysis, and the construction and training of ML and DL models. The automatic deployment and upkeep of the ML and DL models should be the last stage in the lifecycle of AI development.
  • The crucial layer of an API management platform should be used in conjunction with all of these processes to ensure complete integration of the various technologies via APIs and for external integrations, including data sources.

In conclusion, the common layer of security and administration for all these new technologies, including open source stacks, DevOps tools, and AI, is the API management layer. In this day and age, APIs are omnipresent, and the contemporary technology stacks will be connected via APIs, with data technologies (databases and storage), DevOps, and AI leading the charge. There should be a security-first API strategy led by API management.

Don’t forget to keep security in mind while designing and managing APIs. Regardless of whether a headless architecture or new technology-based API approach is chosen for modernization, the API strategy must complement your current technology choices and long-term goals.