Gewape Cloud
Enterprise ready

Sovereign AI infrastructure for enterprise workloads

Gewape Cloud Infrastructure provides enterprise AI infrastructure for teams that need GPU compute, high-performance storage, GPU Kubernetes, private model serving, and in-country AI governance under a documented operating model.

Why this matters

AI workloads are now regulated infrastructure

Enterprise AI is no longer only a lab workload. Banks, governments, healthcare providers, telcos, universities, and AI companies need infrastructure that can support training, fine-tuning, inference, data pipelines, model storage, and audit controls without sending sensitive data into generic offshore environments.

Gewape Cloud Infrastructure approaches AI infrastructure as a sovereign enterprise environment: compute, storage, networking, orchestration, security, and operations are scoped around the customer's workload, jurisdiction, performance needs, and governance obligations.

Enterprise delivery foundation

Compute, storage, Kubernetes, networking, DNS, key management, and database services provide the base platform for enterprise AI environments.
AI infrastructure is delivered through scoped enterprise engagements where region, capacity, hardware profile, support model, and pricing are confirmed in writing.
Country pages and compliance guides frame the sovereignty, residency, and customer-review evidence required by regulated buyers.
Deployment planning covers capacity, hardware strategy, orchestration, observability, security controls, and customer evidence before production use.

AI infrastructure capabilities

What enterprise teams can scope with Gewape Cloud Infrastructure

These capabilities are delivered through enterprise engagements where the architecture, country placement, capacity, controls, pricing, and operational model are confirmed in writing.

Enterprise ready

GPU compute

Dedicated GPU instances and GPU-backed bare metal for training, fine-tuning, inference, data science, and accelerated analytics.

Enterprise ready

Cluster networking

Low-latency east-west networking for multi-node AI jobs, designed around predictable throughput and in-country data paths.

Enterprise ready

AI storage layer

High-throughput shared storage for model checkpoints, datasets, embeddings, logs, and training pipelines.

Enterprise ready

GPU Kubernetes

Managed Kubernetes profiles for GPU workloads, with scheduling, quotas, private networking, and observability.

Enterprise ready

Model serving

Private inference endpoints for enterprises that need latency control, data residency, and auditable model operations.

Enterprise ready

Sovereign AI controls

Policy, access, key custody, logging, and operating controls for AI systems that must stay under local governance.

Enterprise AI engagement

Start with the workload: training, inference, OCR, analytics, model hosting, or regulated AI operations. Gewape Cloud Infrastructure turns that into a deployment plan with compute, storage, networking, security, residency, and support terms.

Request enterprise AI plan

Confirmed in the service scope

Hardware profile, GPU capacity, storage performance, and network fabric are confirmed in the customer service scope.
Private networking, key custody, IAM, logging, backup, and support-access boundaries are documented before production use.
Enterprise teams receive an architecture path for training, fine-tuning, inference, data science, and regulated AI operations.
Capacity, residency, service levels, pricing, and evidence packs are handled through written commercial and technical terms.