How to Build a Production AI Platform With Kubernetes, GitOps, and IaC

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A technical guide outlines how to integrate Kubernetes, GitOps, Infrastructure as Code (IaC), security, and observability into a unified production AI platform. The article, the final part of a series on AI infrastructure for cloud engineers, argues that individual components like GPU scheduling, model serving, and FinOps must be combined into a cohesive, governed system. It emphasizes that infrastructure should be defined as code to make it version-controlled, reproducible, and auditable, rather than configured manually. According to CNCF's 2025 survey, 66% of organizations hosting generative AI models already use Kubernetes for at least some inference workloads. The guide also recommends separating CI pipelines from deployment workflows using GitOps to limit credential exposure and improve deployment control.

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ProgrammingDEV Community ·

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