Cloud Cost Predictability Is the New Competitive Advantage
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AI Infrastructure That Works For You
Deploy AI workloads with confidence. Whether you want us to fully manage your GPU clusters or prefer to operate them yourself with our 24/7 expert guidance, we've got you covered. Run in our data centers or yours—same upstream expertise either way.
24/7
Expert Support
100%
Upstream Kubernetes
0
Vendor Lock-in
Trusted by engineering teams at
Why VEXXHOST
GPU Infrastructure From AI Practitioners
We don't just provide infrastructure—we bring deep expertise in deploying and operating AI workloads at scale. Our engineers understand the unique challenges of GPU computing, model training, and inference pipelines.
GPU Infrastructure Experts
Our team has deployed GPU clusters for leading AI companies. We understand CUDA, driver management, and GPU scheduling at a deep level.
Rapid Deployment
Get your AI infrastructure running faster. Our experience means fewer surprises and faster time-to-production for your ML pipelines.
Data Sovereignty
Train models on your data without it leaving your control. Run on-premise or on sovereign cloud with full compliance.
Kubernetes-Native
100% upstream Kubernetes with GPU operator support. No proprietary forks, full compatibility with your existing ML tools.
24/7 Expert Support
Our engineers are available around the clock. When your training job fails at 3 AM, we're here to help you debug it.
Transparent Pricing
No surprise egress fees or hidden costs. Predictable pricing so you can budget your AI projects with confidence.
Flexible Engagement
Choose How You Want to Work
Mix and match our infrastructure and engagement models to fit your needs. Run hosted or on-premise. Get expert support or go fully managed. The choice is yours.
Infrastructure
Cloud
On-Premise
Engagement
Any Cloud, Your Control
Run GPU clusters on any major cloud while you maintain control. Our AI engineers provide 24/7 CUDA and optimization guidance.
- AWS, GCP, Azure, OpenStack
- 24/7 GPU expert guidance
- Full operational control
Your Data Center, Our Expertise
Keep GPUs on-premise for full data sovereignty. Our engineers provide the same expert AI guidance wherever you run.
- Full data sovereignty
- Train on sensitive data
- Same GPU expertise
Recommended
Any Cloud, Fully Managed
Focus on your models while we manage GPU infrastructure on any cloud. Drivers, monitoring, and scheduling—all handled.
- AWS, GCP, Azure, OpenStack
- Zero GPU ops burden
- Optimized scheduling
Your Data Center, Fully Managed
Run in your data center with hands-off GPU management. We operate your AI infrastructure remotely.
- Complete data control
- Hands-off GPU management
- Remote operations
Cloud Infrastructure
Any Cloud, Your Control
Run GPU clusters on any major cloud while you maintain control. Our AI engineers provide 24/7 CUDA and optimization guidance.
- AWS, GCP, Azure, OpenStack
- 24/7 GPU expert guidance
- Full operational control
Recommended
Any Cloud, Fully Managed
Focus on your models while we manage GPU infrastructure on any cloud. Drivers, monitoring, and scheduling—all handled.
- AWS, GCP, Azure, OpenStack
- Zero GPU ops burden
- Optimized scheduling
On-Premise Infrastructure
Your Data Center, Our Expertise
Keep GPUs on-premise for full data sovereignty. Our engineers provide the same expert AI guidance wherever you run.
- Full data sovereignty
- Train on sensitive data
- Same GPU expertise
Your Data Center, Fully Managed
Run in your data center with hands-off GPU management. We operate your AI infrastructure remotely.
- Complete data control
- Hands-off GPU management
- Remote operations
Challenges We Solve
Why AI Projects Stall
Building AI infrastructure is complex. Here's how we help you overcome the most common obstacles.
Challenge
GPU clusters are expensive and complex to set up. CUDA version conflicts, driver issues, and hardware failures derail training runs.
How We Solve It
Our engineers manage GPU infrastructure daily for AI companies. We handle driver updates, CUDA compatibility, and hardware issues so your team trains models, not infrastructure.
Challenge
Hyperscaler GPU costs spiral out of control. A single training run can cost more than your monthly budget, plus surprise egress fees.
How We Solve It
Transparent pricing with zero egress fees—on AWS, GCP, Azure, or OpenStack. We help optimize GPU utilization so you're not paying for idle compute.
Challenge
Data privacy regulations block public cloud for AI training. Your legal team won't approve sending sensitive data to hyperscalers.
How We Solve It
Train on sovereign infrastructure—your data center or ours. Same Kubernetes-native ML stack, same expert support, complete data control.
Challenge
Provisioning GPUs takes weeks while your ML team waits. By the time infrastructure is ready, priorities have shifted.
How We Solve It
Self-serve GPU resources through Kubernetes. Our pre-configured ML environments mean data scientists get compute in minutes, not weeks.
Use Cases
AI Workloads We Power
From startups training their first models to enterprises running production inference at scale.
Model Training
Train large language models, computer vision systems, and custom ML models with distributed GPU computing.
Data Processing
Process and transform massive datasets for ML pipelines with scalable compute and storage.
MLOps Pipelines
Build end-to-end ML pipelines with Kubeflow, MLflow, and other Kubernetes-native tools.
Real-time Inference
Deploy models for production inference with low-latency GPU serving and auto-scaling.
Development Environments
Provide data scientists with on-demand Jupyter notebooks and GPU development environments.
Batch Processing
Run large-scale batch inference and model evaluation jobs across GPU clusters.
Powered By
Your AI Stack
Open source infrastructure for AI workloads—from training clusters to inference endpoints.
These products integrate seamlessly. Use any combination to build your ideal infrastructure.
Ready to Accelerate Your AI?
Talk to our AI infrastructure experts. We'll help you design a solution that fits your workloads, budget, and compliance requirements.
What you'll get
- Deploy GPU clusters on any cloud or on-premise in your data center — with consistent APIs and management whether you run NVIDIA A100s, H100s, or AMD MI300X
- Train models on sovereign infrastructure with full data control — your training data never leaves your jurisdiction when compliance requires it
- Scale from experimentation to production with Kubernetes-native GPU scheduling — auto-scale training jobs and serve models with the same platform
- Get 24/7 support from engineers who've deployed GPU clusters for AI companies — we debug CUDA issues and optimize GPU utilization, not just restart services
Get in Touch
Tell us about your needs
Blog
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