Kubernetes adoption is skyrocketing as the cloud-native developer community approaches 20 million participants, marking a pivotal moment for an ecosystem that increasingly serves as the substrate for AI.

Platform engineering, abstraction layers and evolving developer personas are reshaping how organizations interact with cloud-native technologies. The latest “State of Cloud Native Development” report from the Cloud Native Computing Foundation and SlashData Ltd. found that Kubernetes adoption is expanding well beyond traditional backend teams, according to Liam Bollmann-Dodd (pictured, left), principal market research consultant at SlashData.

“We looked at our data, and we told CNCF we don’t think backend people are the only people we should be asking about cloud-native technologies,” Bollmann-Dodd told theCUBE. “We did this and, suddenly, boom. We had something like 15 million developers [added]. There are a lot of people doing cloud-native who are not in backend services … who don’t actually configure clouds themselves but are suddenly using all these tools.”

Bollmann-Dodd and Bob Killen (right), senior technical program manager at the CNCF, spoke with theCUBE’s Rebecca Knight and Rob Strechay at KubeCon + CloudNativeCon EU. They discussed Kubernetes adoption trends and how AI workflows are converging with cloud-native infrastructure. (* Disclosure below.)

Kubernetes adoption expands as platform engineering redefines developer roles

The report’s figure of nearly 20 million figure reflects a broader definition of cloud-native developers, expanding beyond traditional backend and infrastructure roles to include practitioners who use cloud-native tools through platforms, dashboards and other abstractions. The jump in Kubernetes adoption and cloud-native developer counts reflects not just new entrants, but a fundamental broadening of who qualifies as a cloud-native developer. Many of these practitioners use tools such as OpenTelemetry without even realizing it, interacting through dashboards and abstracted interfaces rather than direct configuration, Killen explained.

“Cloud-native technologies [have] become so much more accessible and abstracted away,” Killen said. “We have a lot more people that are consuming the technology indirectly. It’s the same when we see the container usage actually go down. But it’s not that it’s actually really going down. It’s just that people aren’t interacting with it directly.”

That abstraction is being driven by the rise of platform engineering, which is gaining organizational traction at a rapid pace. The share of developers reporting no developer operations standardization or a platform at their organization has fallen to just 12%, Bollmann-Dodd noted. The research also revealed a growing divide: Organizations with well-established engineering teams tend to integrate AI workflows into existing platforms rather than building separate infrastructure, a signal that platform engineering maturity shapes AI deployment strategy.

“The people who are the most competent to [build separate AI workflows] — those who have strong development teams, strong internal development teams to build out platforms — they’re the ones choosing not to do this,” Bollmann-Dodd said. “To me that signals that there is a philosophical approach to being a platform engineer. You do not want to keep building separate systems. You want to put it all in together.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of KubeCon + CloudNativeCon EU:

(* Disclosure: The Cloud Native Computing Foundation sponsored this segment of theCUBE. Neither the Cloud Native Computing Foundation nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE