KServe

Standardized, Scalable, and Performant Model Serving on Kubernetes

Visit Website →

Overview

KServe is a Kubernetes-native platform for deploying and serving machine learning models. It provides a standardized and scalable way to run inference for a wide range of ML frameworks. KServe is built on top of Knative and Istio, which enables features like serverless autoscaling and advanced traffic management.

✨ Key Features

  • Kubernetes-native model serving
  • Serverless autoscaling with scale-to-zero
  • Support for multiple ML frameworks
  • Canary rollouts and traffic splitting
  • Pluggable architecture for custom runtimes
  • Open-source and community-driven

🎯 Key Differentiators

  • Built on Knative for serverless autoscaling
  • Standardized and simple API for model serving
  • Strong open-source community and governance

Unique Value: A standardized and scalable open-source platform for serverless machine learning inference on Kubernetes, enabling efficient and cost-effective model serving.

🎯 Use Cases (4)

Deploying and serving machine learning models at scale Building scalable and resilient inference services Automating the deployment of ML models Managing the lifecycle of models on Kubernetes

✅ Best For

  • Real-time prediction APIs
  • Batch inference pipelines
  • Serving models in a multi-cloud environment

💡 Check With Vendor

Verify these considerations match your specific requirements:

  • Users without a Kubernetes infrastructure

🏆 Alternatives

Seldon BentoML MLflow

Provides a more serverless and event-driven approach to model serving compared to other Kubernetes-based platforms, with a focus on simplicity and standardization.

💻 Platforms

API CLI

🔌 Integrations

Kubernetes Knative Istio TensorFlow PyTorch Scikit-learn ONNX

🛟 Support Options

  • ✓ Live Chat

🔒 Compliance & Security

✓ GDPR

💰 Pricing

Contact for pricing
Free Tier Available

Free tier: KServe is open-source and free to use.

Visit KServe Website →