AI workloads are increasingly running on Kubernetes in production, but for many teams, the path from a working model to a reliable system remains unclear.

CNCF's platform is a leading organization driving cloud-native technologies and standards, offering insights into container orchestration, microservices architecture, and cloud-native infrastructure. Through whitepapers, case studies, and community events, CNCF provides insights into adopting cloud-native practices and technologies. Developers and DevOps teams can learn about Kubernetes, Prometheus, and other CNCF projects to build and operate scalable and resilient cloud-native applications.

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AI workloads are increasingly running on Kubernetes in production, yet many teams struggle to bridge the gap between a working model and a reliable system. The CNCF ecosystem provides key building blocks: Dynamic Resource Allocation (DRA) for GPU scheduling, the Gateway API Inference Extension for inference routing, OpenTelemetry and Prometheus for AI-specific observability metrics, Kubeflow for ML workflows, and OPA/SPIFFE for policy and security. Only 41% of professional AI developers currently identify as cloud native, revealing a cultural gap between AI practitioners and platform engineers. The post outlines practical starting points for both audiences and argues that open source composability, portability, and community-driven evolution make the cloud native stack uniquely suited for production AI infrastructure.

The platform under the model: How cloud native powers AI engineering in production