托管 Kubernetes

Vultr Kubernetes Engine – 使用托管 K8s 扩展 AI 应用

在几分钟内配置生产级 Kubernetes 集群。VKE 管理控制平面，让您专注于部署 AI 和云原生应用。

Vultr Kubernetes Engine Features

☸️

Fully Managed Control Plane

VKE provisions and manages etcd, the Kubernetes API server, and scheduler automatically. No manual control-plane maintenance required.

🤖

GPU Worker Nodes

Attach NVIDIA A100 and H100 GPU-backed instances as worker nodes for AI inference pods, distributed training jobs, and GPU-accelerated workloads.

📦

Container Registry Integration

Pull container images directly from Docker Hub, GitHub Container Registry, or Vultr's own container registry with minimal configuration.

🔄

Auto-Scaling Node Pools

Horizontally scale worker node pools based on CPU, memory, or custom metrics. Handle traffic spikes without manual intervention.

🌐

Multi-Region Deployment

Deploy clusters across Vultr's global network of 32+ data center locations. Run workloads close to your users for minimal latency.

🔒

Private Networking & RBAC

Isolated VPC networking per cluster with Kubernetes RBAC, network policies, and TLS-secured API endpoints for production-grade security.

VKE vs EKS vs GKE – Cost Comparison

Feature	Vultr VKE	AWS EKS	Google GKE
Control Plane Fee	Free	$0.10/hr (~$73/mo)	$0.10/hr (~$73/mo)
Min. Node Cost	~$2.50/mo	~$30/mo (t3.small)	~$25/mo
GPU Node Support	A100 & H100	p3, p4d, trn1	A100, H100, L4
Free Tier	Credits via referral	12-mo AWS Free Tier	$300 GCP trial
Global Regions	32+	33 regions	40+ zones

What to Build with Vultr Kubernetes

🧠

AI Inference APIs

Deploy LLM inference servers (vLLM, TGI) as Kubernetes deployments with GPU node pools and horizontal pod autoscaling.

🔬

ML Training Jobs

Run distributed PyTorch training using Kubernetes Job resources with multi-GPU A100 or H100 worker nodes and NVLink connectivity.

🏗️

Microservices Architecture

Orchestrate complex multi-service AI applications with service mesh, ingress controllers, and Kubernetes-native service discovery.

📊

Data Pipelines

Run Apache Spark, Airflow, or Argo Workflows as Kubernetes workloads for scalable ML data ingestion and preprocessing pipelines.

Quick Start: Deploy GPU Pods on VKE

Install kubectl & Vultr CLI

curl -LO https://dl.k8s.io/release/stable.txt | xargs -I{} curl -LO https://dl.k8s.io/release/{}/bin/linux/amd64/kubectl

Create VKE Cluster via API

curl -X POST "https://api.vultr.com/v2/kubernetes/clusters" \
  -H "Authorization: Bearer $VULTR_API_KEY" \
  -d '{"label":"ai-cluster","region":"ewr","version":"v1.29.0","node_pools":[{"node_quantity":2,"plan":"voc-g-2c-8gb-75s-amd"}]}'

Deploy GPU Inference Pod

kubectl apply -f - <<EOF
apiVersion: v1
kind: Pod
metadata:
  name: llm-inference
spec:
  containers:
  - name: vllm
    image: vllm/vllm-openai:latest
    resources:
      limits:
        nvidia.com/gpu: 1
EOF