Vultr Kubernetes Engine – Escale Apps de IA com K8s Gerenciado
Provisione clusters Kubernetes de nível produção em minutos. O VKE gerencia o control plane para que você foque em implantar apps de IA e nativas de nuvem.
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
curl -LO https://dl.k8s.io/release/stable.txt | xargs -I{} curl -LO https://dl.k8s.io/release/{}/bin/linux/amd64/kubectlcurl -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"}]}'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
EOFRelated Technical Guides
Related Infrastructure Pages
FAQ do Vultr Kubernetes
What is Vultr Kubernetes Engine (VKE)?
VKE is Vultr's managed Kubernetes service that provisions and manages the Kubernetes control plane for you. You deploy worker nodes (including GPU nodes) and VKE handles etcd, API server, and cluster upgrades automatically.
Can I attach GPU nodes to a VKE cluster?
Yes. Vultr allows you to add GPU-backed compute instances as worker nodes in your Kubernetes cluster, enabling GPU-accelerated workloads like AI inference pods and distributed training jobs.
How does VKE compare to EKS and GKE?
VKE is significantly cheaper than EKS or GKE for equivalent compute, with no per-cluster management fees. It is ideal for startups, AI projects, and teams seeking predictable cloud costs without sacrificing Kubernetes features.
What Kubernetes versions does VKE support?
VKE supports the latest stable Kubernetes releases and provides regular version upgrades. You can upgrade clusters with minimal downtime using rolling node updates.
Ready to Deploy on Vultr Kubernetes?
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