vLLM Production Stack

High-throughput LLM serving engine

vLLM is an Apache-2.0 LLM inference and serving engine focused on high-throughput self-hosted model APIs. It combines PagedAttention, continuous batching, prefix caching, quantization options, OpenAI-compatible serving, structured outputs, metrics, Docker/Kubernetes deployment guidance and integrations with agent and LLM frameworks.

Kubernetes-native model inference platform

KServe is an open-source Kubernetes-native platform for deploying and managing ML model inference at scale. It provides standardized inference protocols, autoscaling including scale-to-zero, canary rollouts, A/B testing, and multi-model serving. KServe supports all major ML frameworks including TensorFlow, PyTorch, scikit-learn, XGBoost, and LLM runtimes like vLLM and Triton through pluggable serving runtimes.

Kubernetes-native distributed LLM inference stack

llm-d is an open-source Kubernetes-native stack for distributed LLM inference with cache-aware routing and disaggregated serving. It separates prefill and decode stages across different GPU pools for optimal resource utilization, routes requests to nodes with warm KV caches, and integrates with vLLM as the serving engine. Apache-2.0 licensed with 2,900+ GitHub stars.

Cloud-native control plane for scalable GenAI inference

Open-source Kubernetes-native building blocks for deploying, routing and scaling GenAI inference, including an LLM gateway, autoscaling, LoRA management and KV-cache offloading.

Kubernetes operator for serving AI inference workloads

KubeAI is an Apache-2.0 Kubernetes operator for deploying and scaling AI inference workloads, including LLMs, embeddings, reranking, and speech-to-text. It gives platform teams OpenAI-compatible endpoints, model proxy/controller primitives, model caching, scale-from-zero behavior, and cluster-native resource management for self-hosted inference on Kubernetes.