As a greater number of companies move their infrastructure and applications to the cloud, the way they interact with APIs is also changing. The shift toward cloud-native architectures, as well as multi-cloud and hybrid cloud structures, has widely impacted how APIs are created, deployed, and managed. One outcome is increased complexity across the DevOps community, which is also working feverishly to digest, understand, and integrate the proliferation of AI-based tools into the mix.
What does it all mean for developers? And what should we expect this year and beyond? Let's jump in.
Growth Is the Common Denominator
By making it more effective and less costly to spin up APIs that are available and highly performant, cloud-native architectures are helping to drive growth in the API economy. And, using cloud architectures, companies can deploy their APIs in multiple regions, making it easy and cost-effective to serve users around the world.
At the same time, the API boom has contributed to the popularity of cloud architectures because APIs enable developers to streamline and automate the process of interacting with cloud-based solutions. APIs play a crucial role, for instance, in allowing systems and applications to communicate with cloud storage services, and making the cloud storage run optimally by providing better management, automation, integration, and monitoring capabilities.
These symbiotic improvements do not come without complications, however. Managing the increasing complexity of the exploding number of APIs and cloud-based solutions — along with the introduction of artificial intelligence (AI) tools — promises to be a top challenge for developers this year.
Unpacking the Complexity
While APIs in multi-cloud environments give organizations increased flexibility and greater options for functionality, these benefits come with a price: increased complexity. More pressure is being placed on the DevOps world to not only understand the software and applications they build, but also how they actually operate "behind the scenes." Ultimately, the gap between DevOps and software engineers is getting thinner and thinner.
Take an example on the UI side: A single button that a user sees on a dashboard simplifies a host of development complexities happening on the back end. Not only does that button likely call multiple APIs, it also brings into play considerations about the order of actions, the dependencies between different steps, and understanding underlying components. In other words, developers need to orchestrate multiple APIs and process steps on the back end to achieve the same thing that a button achieves on the UI.
The proliferation of APIs and their increased scalability in cloud-native environments also generates greater complexity. Scaling APIs requires companies to be sure they can handle increased loads during traffic spikes or other unexpected events, for instance.
In addition, many companies embracing a cloud-native approach are also moving toward microservices architecture, where independently deployable services replace legacy, monolithic software suites. This increases the number of APIs that companies need to manage and maintain, leading to increased complexity.
It is crucial for companies to understand that integrating and maintaining a large — and likely increasing — load of APIs requires skilled developers who can understand and manage this complexity.
The AI Factor
While the current AI attention is largely focused around consumer-facing tools like ChatGPT and Dall-E, AI also has a place in the cloud-native-driven complexity discussion. Companies can use AI to recognize trends within their cloud-based solutions and allow them to make smarter decisions.
For example, online retailers need to prepare in advance to scale up for Black Friday in order to ensure their various cloud-based applications will function properly with the dramatic increase in activity. AI can ensure that the software will prepare itself and scale before the actual peak, so that operational capacity will not crash or slow down. AI can also help navigate the peaks and troughs of demands at different times of the day. If a company has servers in the U.S, for instance, it can scale down during the overnight hours while scaling up in Europe at that time.
In essence, AI can help organizations make smarter cloud-related decisions that can help DevOps teams by reducing manual workloads.
Moving through this DevOps landscape of increased API usage and cloud-native software adoption, two trends stick out for 2023. First, we will start to see smaller cloud providers running on large cloud-provider infrastructure, providing a niche product to a specific use case. For example, Vercel runs on top of both AWS and GCP (without actively exposing it to its customers) and provides a targeted offering for front-end developers that is highly optimized for their needs. This approach is more user-friendly and enables faster development.
A second trend underway is companies embracing AI to help analyze the costs of cloud architectures. Analyzing multiple cost factors, such as computing, storage, and network resources as well as service, usage, and maintenance, has been a challenging task. In response, this year will bring to fruition more products that use AI to analyze cloud usage and recommend where and how to cut costs.
As APIs and cloud-native architectures and tools continue to advance and increase in usage, developers will need to continue expanding their understanding of the complex interconnections between the two. Knowing how to handle, manage, and even predict the complexities will go a long way in the DevOps community.
Andrey Bukati is Chief Technology Officer of San Francisco-based company, Rapid, the leading API hub provider that enables developers and enterprises to build, consume, and manage APIs. He is experienced in software architecture and development and has held the position of CTO for seven years. In his former roles, Andrey has worked as a senior/full-stack developer at Fifth Dimension Ltd and rollout.io in Israel. He previously founded and ran Weeb, a company specialized in advanced web services, including website development, e-commerce, and mobile apps development.