Architect for Scale: How Serverless Inference Changes AI Deployment | Jon Alexander, Akamai

Organizations often face significant challenges when attempting to untangle their existing infrastructure and transition toward a distributed model, a hurdle that Akamai aims to simplify so enterprises can focus on business outcomes rather than complex plumbing. A key decision in this cloud journey involves early architectural choices; if applications are tied too closely to a single availability zone, region, or database location from the start, organizations will encounter substantial difficulties later on. This principle applies equally to artificial intelligence, where failing to plan for distribution can lead to rigid systems that are hard to scale or adapt as needs evolve. Akamai has heavily invested in serverless technology as a fundamental approach to compute and infrastructure, believing it is crucial for widespread AI adoption. The company is currently developing a serverless inference platform designed to be consumed as a service, allowing customers to deploy models without worrying about specific locations, the number of regions involved, or placement details. By abstracting these underlying complexities, Akamai enables businesses to run AI workloads across many different locations seamlessly, ensuring that the infrastructure supports growth without requiring constant re-engineering of the core system. The most effective strategy is to architect with future scalability in mind, even if the initial deployment occurs in just one region on day one. While it is not necessary to build out all potential infrastructure immediately, having a clear pathway and plan for expansion to two, five, or even hundreds of locations provides essential optionality for later growth. This forward-thinking approach prevents organizations from being locked into suboptimal configurations and allows them to scale their AI deployments efficiently as demand increases across the globe. Ultimately, planning ahead ensures that early decisions do not become bottlenecks, enabling a smooth transition to a robust, distributed AI environment.