KServe
The standard for model serving on Kubernetes, with autoscaling down to zero.
KServe exposes a model as a Kubernetes InferenceService resource: you declare the format (scikit-learn, XGBoost, PyTorch, TensorFlow, ONNX, Hugging Face, or a custom container) and where the weights live, and it provisions the server, the HTTP/gRPC endpoint and autoscaling, down to scale-to-zero through Knative. It handles canary rollouts with traffic splitting, pre/post-processing transformers, explainers and LLM serving with vLLM. It's our choice for serving models in production on an existing Kubernetes cluster without depending on a cloud provider.
What KServe brings to your project.
Typical use cases: Production ML and LLM serving, multi-model inference, in-house MLOps platforms.
- 01
InferenceService: one declarative CRD, from weight files to a live endpoint.
- 02
Traffic-based autoscaling and scale-to-zero, GPUs released when idle.
- 03
Canary rollouts and traffic splitting between versions, instant rollback.
- 04
Ready-made runtimes: scikit-learn, XGBoost, PyTorch, ONNX, Triton, vLLM for LLMs.
Entrust your project
to our experts.
Entrust your project to our
experts.
Entrust your project
to our experts.
Our experts build your project, delivering superior technical and functional quality within shorter timeframes.
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A project with KServe?
Describe your project. Our team replies within 24 hours with free technical scoping, along with a clear estimate of costs and timelines. No commitment.
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Describe your project and we'll get back to you with a costed estimate and a roadmap.