Introduction
KServe is a Kubernetes-native model inference platform that provides standardized CRDs and data-plane protocols to support scalable predictive and generative AI in production.
Key Features
- Standardized Inference CRDs and APIs for simplified model deployment and lifecycle management.
- Autoscaling (including GPU autoscaling and scale-to-zero) and high-density model loading via ModelMesh.
- Support for canary releases, pipelines, and ensembles (InferenceGraph) for advanced deployment patterns.
Use Cases
- Deploy and manage online inference services (real-time and batch) on Kubernetes declaratively.
- Provide a unified ingress and routing layer for multi-framework, multi-model deployments.
- Integrate with GenAI/LLM inference and MCP scenarios with observability and governance.
Technical Highlights
- Extends Kubernetes via CRDs for smooth integration with k8s toolchains and CI/CD.
- Integrates with ModelMesh for intelligent routing, resource reuse, and high-density serving.
- Supports various deployment modes (Knative serverless, raw k8s, ModelMesh) to meet different scale and latency needs.