Verwaltetes Kubernetes

Vultr Kubernetes Engine – KI-Apps mit Managed K8s skalieren

Provisionieren Sie produktionsreife Kubernetes-Cluster in Minuten. VKE verwaltet die Steuerebene, damit Sie sich auf KI- und cloud-native Apps konzentrieren können.

Explore GPU Cloud →

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.

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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.

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Container Registry Integration

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

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Auto-Scaling Node Pools

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

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Multi-Region Deployment

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

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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

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AI Inference APIs

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

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ML Training Jobs

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

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Microservices Architecture

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

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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

Related Technical Guides

→How to Deploy a GPU Server for AI Workloads →Deploying LLMs on Cloud GPUs: Production Guide →Vultr vs AWS GPU: Full Comparison 2026

Related Infrastructure Pages

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GPU Cloud Instances

A100 & H100 – specs and use cases

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AI Model Training

PyTorch, JAX, distributed GPU training

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Object Storage

S3-compatible storage for ML datasets

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HFT / Algo Trading

Bare metal, 10Gbps, sub-ms latency

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Referral Bonus

Up to $300 in promotional credits

Vultr Kubernetes FAQ

Was ist die Vultr Kubernetes Engine (VKE)?

VKE ist Vultrs verwalteter Kubernetes-Service, der die Kubernetes-Control-Plane für Sie provisioniert und verwaltet. Sie stellen Worker-Nodes (einschließlich GPU-Nodes) bereit und VKE übernimmt etcd, API-Server und Cluster-Updates automatisch.

Kann ich GPU-Nodes an einen VKE-Cluster anhängen?

Ja. Vultr ermöglicht es Ihnen, GPU-gestützte Compute-Instanzen als Worker-Nodes in Ihrem Kubernetes-Cluster hinzuzufügen, wodurch GPU-beschleunigte Workloads wie KI-Inferenz-Pods und verteilte Training-Jobs ermöglicht werden.

Wie vergleicht sich VKE mit EKS und GKE?

VKE ist erheblich günstiger als EKS oder GKE für vergleichbare Rechenleistung, ohne Gebühren pro Cluster. Es ist ideal für Startups, KI-Projekte und Teams, die vorhersehbare Cloud-Kosten anstreben, ohne auf Kubernetes-Funktionen zu verzichten.

Welche Kubernetes-Versionen unterstützt VKE?

VKE unterstützt die neuesten stabilen Kubernetes-Releases und bietet regelmäßige Version-Updates. Sie können Cluster mit minimaler Ausfallzeit durch rolling Node-Updates aktualisieren.

Ready to Deploy on Vultr Kubernetes?

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