Edge-forward: Akamai eyes sweet spot between centralized & decentralized AI inference

At KubeCon + CloudNativeCon Europe 2026, Lena Hall and Thorsten Hans of Akamai outlined how the company is evolving from a CDN provider into a developer-focused cloud platform for AI. Akamai’s strategy centers on low-latency, distributed computing, combining managed Kubernetes, serverless functions, and a distributed AI inference platform to support modern workloads.

With a global footprint of core and “distributed reach” datacenters, Akamai aims to bring compute closer to users while still leveraging centralized infrastructure for heavier processing. This hybrid model enables faster feedback loops critical for applications like fraud detection, robotics, and conversational AI.

To address concerns about complexity, Akamai emphasizes managed infrastructure and self-service tools that abstract away integration challenges. Its platform supports open source through managed Kubernetes and pre-packaged tools, simplifying deployment.

Akamai also invests in serverless technologies like WebAssembly-based functions, enabling developers to build and deploy globally distributed applications quickly. Overall, the company prioritizes developer experience, allowing teams to focus on application logic rather than infrastructure management.

Learn more from The New Stack about the latest developments around how Akamai is transforming to a developer-focused cloud platform for AI.

Akamai Picks Up Hosting for Kernel.org

Should You Care About Fermyon Wasm Functions on Akamai?

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

Det här avsnittet är hämtat från ett öppet RSS-flöde och publiceras inte av Podme. Det kan innehålla reklam.

Avsnitt(300)

Bit Cloud’s next chapter starts after the AI builds your app

For many developers, turning an AI-generated prototype into maintainable software requires more than generating code—it requires infrastructure, collaboration, testing and review. In this episode of T...

1 Okt 31min

CloudBees just committed to an AI-first pivot. Here's why it matters for enterprise DevOps teams

CloudBees CEO Mo Plassnig is leading the CI/CD company through a major transformation as generative AI reshapes software development. Returning to CloudBees eight years after joining through its acqui...

30 Sep 23min

A third option is emerging in the fight over AI and your data

The AI industry has faced a growing enterprise dilemma: companies want access to powerful proprietary AI models without risking sensitive data or intellectual property, while AI labs want to protect t...

23 Sep 30min

Drowning in AI pull requests: Harness's field CTO on code review and a Git repo built for agents

Harness Field CTO Martin Reynolds joins The New Stack to talk about what happens after coding agents start opening pull requests faster than anyone can review them. He explains how he first saw the bo...

7 Sep 26min

How to find failures without drowning in tracing data

Traces provide a detailed view of a request’s journey through data, microservices and applications, helping SREs pinpoint where failures occur and resolve issues faster. But while tracing can reduce d...

3 Sep 31min

Why CPUs still matter in the age of AI agents

As AI evolves from conversational chatbots to autonomous agents, CPUs are becoming an increasingly important part of the infrastructure equation. In this episode, The New Stack speaks with Bhumik Pate...

11 Aug 26min

Why Doist Says Less AI Can Deliver More

Doist CTO Gonçalo Silva says AI is reshaping software development, but success depends on restraint rather than rapid feature expansion. Instead of chasing every AI capability, Doist prioritizes “subt...

31 Juli 35min

Why your company should (try to) build its own AI SRE

As AI coding agents accelerate software development, they also create new challenges for site reliability engineers (SREs), who are increasingly responsible for debugging systems that no single human ...

30 Juli 30min