KServe
Standardized, Scalable, and Performant Model Serving on Kubernetes
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)
✅ 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
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
🔌 Integrations
🛟 Support Options
- ✓ Live Chat
🔒 Compliance & Security
💰 Pricing
Free tier: KServe is open-source and free to use.
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