# KServe (/docs/coreai/components/kserve)



Component Category [#component-category]

AI serving / model inference

Component Description [#component-description]

KServe is a Kubernetes-native model serving framework that provides a standardized interface for deploying, scaling, and managing machine learning models. It supports multiple serving runtimes and defines the `InferenceService` custom resource for declarative model deployment. The platform deploys both the KServe CRDs (sync wave 6) and the KServe controller (sync wave 7) as separate components.

Why It Is Used [#why-it-is-used]

In BullSequana AI, KServe provides the target model serving framework for the platform. It offers a standardized, framework-agnostic serving layer with support for custom pre/post-processing, model explainability, and multi-model serving patterns through the `InferenceService` API. KServe is planned to fully replace `KubeAI` as the primary inference orchestration layer.

Learn More [#learn-more]

* [KServe documentation](https://kserve.github.io/website/)
* [kserve/kserve on GitHub](https://github.com/kserve/kserve)

Deployment notes [#deployment-notes]

KServe is split into two platform components: `kserve-crd` (CRDs only, sync wave 6) and `kserve` (controller, sync wave 7). The CRDs must be installed before the controller starts. Both deploy into the `kserve` namespace. The controller creates additional resources for Rook Ceph RGW integration for model storage.

Interacts With [#interacts-with]

* `KubeAI`, which KServe is planned to fully replace as the inference orchestration layer.
* `Rook Ceph`, which provides S3-compatible storage for model artifacts through RGW.
* `Gateway API`, which routes inference traffic to KServe endpoints.