• If you are spending meaningfully on Azure, AWS, or GCP GPU instances and want to reduce cost — or need GPU compute outside the hyperscaler while keeping workloads and data entirely within your own VPC — this is for you.

Most GPU-heavy workloads work out of the box — LLM training, fine-tuning, inference, multi-modal AI, classic ML, statistical modeling, and scientific computation.

• GPU access is over an encrypted SSL connection. Only outbound connections from your infrastructure — no inbound ports required.
• Your containers keep running inside your VPC. No data leaves your private network — only compute cycles move to the GPU.

Your containers are still running inside your VPC — only the compute happens outside. Nothing needs to change in your ACL or networking rules. Your dependencies stay private and accessible as if the GPU is local to your infrastructure.

• Launch time: a one-time increase of a few seconds to a minute, as the GPU attaches and initializes.
• Per-call latency: a few milliseconds per API call your application serves.

Your data is loaded directly into GPU VRAM and never stored outside your VPC. When your workload exits, GPU memory is wiped clean. Data residency requirements are naturally satisfied — nothing persists beyond the lifecycle of your job.

We are SOC 2 Type II compliant. For additional requirements (HIPAA, PCI-DSS, FedRAMP, GDPR, or sector-specific frameworks), reach out at info@cloudexe.tech and we will walk you through how GPU-Bridge fits into your compliance posture.

Very little. Copy our launcher binary inside your container and launch your workload command via it — the rest is automatic.

The GPU-Bridge technology is proven. The same tech stack is actively used by world-class universities for research workloads. You are getting battle-tested GPU virtualization technology applied to your private infrastructure.