kserve

What it is and what it does

KServe is a Python SDK for building and managing model inference services, split into two main components: a server library that standardizes how models are registered, loaded, and served with prediction handlers, and a client library for interacting with KServe control planes to create and manage InferenceService instances on Kubernetes clusters.

The server side handles model loading from multiple storage backends (Google Cloud Storage, S3, Azure Blob Storage, local filesystem, persistent volumes, and HTTP URLs), provides hooks for pre/post-processing and liveness/readiness checks, and emits Prometheus metrics for request latency across each pipeline stage. The client side lets you programmatically create, patch, and delete inference services on a remote KServe cluster. It's designed for teams deploying ML models at scale on Kubernetes, with built-in support for common frameworks and a large dependency footprint reflecting its integration with fastapi, grpcio, kubernetes, and data libraries.

Use it for

• Deploy a trained scikit-learn or PyTorch model as a REST/gRPC inference service on Kubernetes with automatic model loading from cloud storage.
• Build a custom prediction server with pre/post-processing logic (e.g., feature engineering, output transformation) using the standardized handler interface.
• Manage multiple model inference services on a Kubernetes cluster via the Python client, automating service creation and lifecycle operations.
• Monitor inference latency and request throughput using Prometheus metrics exposed at the /metrics endpoint for each prediction stage.
• Load models from diverse storage sources (S3, GCS, Azure, local files, HTTP URLs) without writing custom download logic.

Worth the install?

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Install

kserve on PyPI

Before you install

Low install friction with a pure Python wheel. Active maintenance with a recent release (8 days old) and steady repository activity. Requires Python 3.10–3.12; pulls in 28 runtime dependencies including fastapi, kubernetes, grpcio, and data processing libraries, which may add complexity to your environment.

Requires Python 3.10 or later (capped below 3.13). Kubernetes cluster and appropriate cloud credentials (AWS, GCP, Azure) needed if using remote storage backends.

License in practice

Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for most production and proprietary projects.

Quickstart

pip install kserve

from kserve import KServeModel

class MyModel(KServeModel):
    def predict(self, request):
        return {"predictions": request["instances"]}

if __name__ == "__main__":
    model = MyModel("my-model")
    model.load()
    model.start()

Package facts

Evidence: kserve-0.20.0-py3-none-any.whl

Tags

More Software Development packages

See also sagemaker-inference · mlserver · tensorflow-serving-api · sagemaker-serve · bentoml · azureml-inference-server-http · google-cloud-container · kfp-kubernetes · matrice · multi-model-server