KServe

KServe helps teams turn trained machine-learning models into services that applications can call. It supplies Kubernetes resources and components for managing the serving deployment, while a chosen runtime actually loads the model and handles inference requests. It is useful when several models need a common operating platform. Installation modes have different dependencies, so model storage, networking, identity and scaling must be chosen as part of that platform design.

Map the complete path

Record the inference runtime, model storage, identity, gateway, autoscaling mechanism and any required supporting controllers. Verify the exact release's installation sequence and charts; older tutorials can describe a different bundle of dependencies.

Acceptance criteria

Test a model's initial load, warm requests, scale-up, scale-down and backend failure. Measure time to first token and queue delay for generative workloads, not only HTTP success. Ensure model credentials and endpoint authorization have separate owners and are rotated without exposing tokens in logs.

KServe is a platform choice rather than a substitute for evaluating the model server. Compare it with a focused vLLM deployment and Ray-based services in the inference guide.