As AI workloads move into production, Kubernetes platform vendors face a growing interoperability challenge. Accelerator management, distributed scheduling, inference networking, observability, security, and AI operators often behave differently across platforms, creating friction for both vendors and end users.

In this session, we will share a vendor’s journey toward Kubernetes AI Conformance, from assessing platform readiness and mapping product capabilities to conformance requirements, to identifying gaps and producing verifiable evidence.

We will also discuss what the conformance program validates, what remains outside its scope, and where vendors may encounter ambiguity as the requirements continue to evolve. Attendees will leave with a practical framework for evaluating an existing Kubernetes platform, planning conformance work, and translating certification requirements into measurable portability, reliability, and customer value.