Standardizing cloud-native AI platforms is a key step in advancing the AI infrastructure ecosystem.
In recent years, the cloud-native ecosystem has gradually expanded from general-purpose computing to AI workloads. The CNCF (Cloud Native Computing Foundation) is driving a new certification initiative— Kubernetes AI Conformance —aimed at establishing a set of technical standards for AI platforms to be compatible and interoperable with Kubernetes.
This certification seeks to answer a core question:
“What does it take for an AI platform to be truly Kubernetes-native?”
Why AI Conformance Is Needed
Currently, many AI platforms claim to “run on Kubernetes,” but their actual integration varies greatly. Here are some common scenarios:
- Some platforms merely run containers on Kubernetes without deep integration with the control plane.
- Others fully integrate with Kubernetes control plane, scheduling, and observability systems.
- Many vendors build their own controllers, schedulers, and storage interfaces, resulting in migration and interoperability challenges across environments.
The core purpose of CNCF’s AI Conformance is to unify standards so that AI platforms behave consistently across clouds and clusters, becoming a common language for the ecosystem—much like “Certified Kubernetes.”
Key Criteria for Kubernetes-Native AI Platforms
A Kubernetes-native AI platform must meet several key criteria:
Architecture-Native: Everything as Kubernetes Objects
For AI training, inference, and batch processing scenarios, all tasks should be declared as Pod, Job, or CRD (Custom Resource Definition) objects. Scheduling, scaling, and lifecycle management should be handled by the Kubernetes control plane, not by custom platform logic.
For example, Kubeflow Training Operator, RayCluster CRD, and vLLM Operator all use this native object declaration approach.
Scheduling-Native: Unified Compute Resource Scheduling
AI platforms need to collaborate with Kubernetes Device Plugin and Scheduler to detect GPU, NPU, and other heterogeneous compute resources, supporting resources.requests/limits for resource management. Task scheduling should be observable and traceable, avoiding black-box operations.
Storage-Native: Declarative Data and Model Access
Data and model access should not rely on host paths but use PVC (PersistentVolumeClaim), CSI (Container Storage Interface), S3/NAS, and other standard interfaces for mounting. Credentials and sensitive parameters should be injected via Secrets and ConfigMap. The entire pipeline should be reproducible via GitOps/CI/CD workflows, ensuring traceability and automation.
Network and Service-Native: Compatible with Mesh and Gateway
AI inference services should be exposed as standard Service, Ingress, or Gateway API resources, supporting multi-cluster service discovery and routing policies, and integrating seamlessly with service meshes like Istio, Envoy, and Linkerd.
Additionally, platforms should output standardized monitoring metrics (e.g., Prometheus), logs (e.g., FluentBit), and tracing data (e.g., OpenTelemetry) for unified observability and operations.
Portability and Interoperability
A truly Kubernetes-native AI platform should behave consistently across environments, including:
- Public clouds (EKS, GKE, ACK)
- Private clouds (OpenShift, KubeSphere)
- Bare-metal clusters
The platform should also integrate directly with mainstream ecosystem components such as Kubeflow, Ray, KServe, and Triton, achieving high interoperability.
CNCF’s Goal: From “Running on Kubernetes” to “Growing within Kubernetes”
CNCF aims to use the AI Conformance certification mechanism, much like Certified Kubernetes, to drive the AI infrastructure ecosystem toward standardization.
In the future, the industry may see:
- The Certified AI Platform badge as a trust mark for platforms.
- Automated verification bots (Verify Conformance Bot) to improve testing efficiency.
- Multi-version test suites (e.g., v1.33, v1.34) to ensure compatibility.
These measures will become important technical thresholds and trust foundations for cloud vendors, AI platforms, and open-source AI infrastructure projects.
Summary
In the AI era, standardization is the foundation for ecosystem evolution. For AI platforms to thrive in the cloud-native world, they must not only “run on Kubernetes” but also “grow within Kubernetes.”
A truly Kubernetes-native AI platform should feature:
Control plane compatibility, transparent data plane, declarative extensibility, portability, observability, and reproducibility.
This is the key intersection of AI and cloud-native—and the foundation for the next generation of AI infrastructure.