This diagnostic is a quick self-assessment built for enterprise AI practitioners — including engineers, architects, and infrastructure leaders — managing live AI inference workloads in production. It is designed to act as a diagnostic of your organization's current operational status and a guide for navigating the infrastructure constraints that come next.
Find your AI infrastructure stage and what to prioritize next
AI Adoption Assessment
Take this quick Akamai diagnostic to discover your AI infrastructure adoption stage, identify operational bottlenecks, and unlock the next phase of scale.
Frequently Asked Questions (FAQ)
Frequently Asked Questions (FAQ)
The diagnostic maps your organization across four progressive operational stages: Integrate (API-first), Operate (self-hosted), Optimize (purpose-built), and Serverless (edge native). These stages act as behavioral proxies to understand how your deployment strategy, technical requirements, and operational hurdles evolve as your AI workloads scale.
As you transition to serving custom, fine-tuned models at millions of requests per day, you face strict global SLA requirements that demand sub-250 ms response times. Centralized compute infrastructure creates physical distance between data centers and international users. This distance introduces network latency that internal model optimization cannot resolve.