How OCI Artifacts Will Drive Future AI Use Cases

The role of OCI artifacts in the AI ecosystem How Kubernetes and OCI Artifacts fit together CRI-O and OCI Artifact support Help to shape an AI native Kubernetes Posted on August 27, 2025 by Sascha Grunert, CNCF Member Project Maintainer (Graduate Project) CNCF | Special Interest Group (SIG) | CNCF Ambassador In recent years, the software industry has seen a strong shift toward enabling and supporting Artificial Intelligence (AI) workloads. While a variety of high level tools like Large Language Models (LLMs) already exist to support generic use cases, many domain specific solutions are either not yet available or come with significant development costs and risks, particularly when targeting more niche problems. This raises an important challenge: how can we avoid building a fragmented AI tooling landscape with limited real world applicability? To truly support the next wave of AI innovation, especially in cloud native environments, we need to rethink and reinforce our software component foundation. This includes standardizing formats, improving cross platform interoperability, and evolving Kubernetes with AI native features in mind. One of the most promising developments in this space revolves around Open Container Initiative (OCI) artifacts. OCI artifacts enable users to store and distribute arbitrary files and metadata using OCI compliant container registries. While originally used for generic purposes (like ORAS supports them), they’re now finding critical roles in AI/ML workflows, especially with the rise of specifications like the CNCF ModelPack. The CNCF ModelPack Specification builds on top of OCI artifacts and aims to standardize the packaging, distribution, and execution of AI models in cloud native environments. By moving away from proprietary formats, ModelPack facilitates reproducibility, portability, and vendor neutrality in machine learning workflows. This opens the door to several important use cases: Standardized AI/ML model packaging : With OCI artifacts, models can be versioned, distributed, and tracked like container images. This promotes consistency and traceability across environments. Secure model delivery : Well established solutions like sigstore signatures offer mechanisms to sign and verify OCI artifacts, improving model integrity and trustworthiness.