KServe provides distributed inference for generative and predictive AI models on Kubernetes.
Open source
21 alternativesKserve
Deploy generative and predictive AI models on Kubernetes with KServe’s distributed inference platform.
About Kserve
KServe is an open-source platform for deploying generative and predictive AI models on Kubernetes. It provides a standardized setup for multi-framework model inference.
For generative models, it supports vLLM and llm-d backends, an OpenAI-compatible protocol, GPU use, and Hugging Face models. For predictive models, it supports frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, and ONNX, with routing, canary rollouts, explanations, and monitoring. The standard Kubernetes installation does not support canary deployment or request-based autoscaling with scale-to-zero.
Key features
- Serve large language models using vLLM and llm-d backends
- OpenAI-compatible inference protocol for language models
- Deploy Hugging Face models with streamlined workflows
- Serve TensorFlow, PyTorch, scikit-learn, XGBoost, and ONNX models
- Route requests between predictor, transformer, and explainer components
- Request-based autoscaling with scale-to-zero for predictive workloads
Best for: Teams that deploy generative and predictive models on Kubernetes and need a common inference setup.
Good for
- Covers generative and predictive inference in one platform
- Supports multiple predictive frameworks and LLM backends
- Includes scale-to-zero for predictive workloads when not in use
Watch out for
- Requires Kubernetes to deploy and run models
- Standard Kubernetes installation does not support canary deployment
- Standard Kubernetes installation does not support request-based autoscaling with scale-to-zero
Kserve at a glance
- Website
- github.com/kserve/kserve
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Kserve alternatives
Tools and sites you could use instead, ranked by how closely they match.
FedML supports distributed model training, model serving, and federated learning, with a scheduler for AI jobs across cloud and local clusters.
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GraphPipe provides a protocol and software for deploying machine learning models separately from their frameworks.
PowerInfer supports serving large language models for local deployment and inference.
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NVIDIA Triton Inference Server provides an inference solution for deploying models in cloud and edge environments.
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Sonar provides large-scale inference for language models through the project hosted in this repository.
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Kserve FAQ
Which predictive frameworks can KServe serve?
TensorFlow, PyTorch, scikit-learn, XGBoost, ONNX, and more.
Does the standard Kubernetes installation support canary deployments and scale-to-zero autoscaling?
No. The standard Kubernetes installation does not support canary deployment and request-based autoscaling with scale-to-zero.
What backends does KServe support for large language models?
vLLM and llm-d, with an OpenAI-compatible inference protocol.
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