Private AI, on your terms.
Run AI with sovereignty and real-time performance on a composable private cloud. whitesky.cloud unifies a full cloud stack with GPU & memory pooling for elastic power—without sending your data off-prem.
The Private AI Challenge
Enterprise and public-sector organisations face mounting pressure to deploy AI whilst maintaining data sovereignty, managing unpredictable cloud costs, and ensuring compliance with GDPR and NIS2 regulations. Traditional approaches force difficult compromises between performance, control, and cost.
Unpredictable Costs
Public cloud GPU pricing fluctuates wildly, making budget planning impossible whilst idle capacity drains resources.
Data Residency Concerns
Regulatory compliance demands on-premises processing, yet traditional infrastructure lacks the elasticity for AI workloads.
Resource Islands
GPU and memory resources sit in silos, preventing optimal utilisation and creating bottlenecks for critical workloads.
Slow Provisioning
Traditional infrastructure requires days or weeks to provision resources for time-sensitive AI projects.
The whitesky.cloud Solution
Composable Acceleration
Dynamically assemble 1–30+ GPUs per job across hosts through our intelligent fabric. Resources return to the pool instantly after job completion, maximising utilisation whilst eliminating waste.
Elastic Memory on Demand
Allocate up to tens of TB of RAM to any workload via pooled memory architecture. Perfect for giant language models and in-memory AI applications requiring massive datasets.
Full-Stack Control Plane
Self-service VMs and containers, S3-compatible object storage, software-defined networking, multi-tenancy, comprehensive FinOps with chargeback, and unified IAM/RBAC.
Sovereign Low-Latency
On-premises and edge inference with single-digit millisecond latency whilst maintaining full GDPR and NIS2 compliance. Your data never leaves your control.
How It Works
Full Cloud Stack
whitesky.cloud provides on-demand VMs and containers, S3-compatible object storage, software-defined networking, and multi-tenancy through Cloudspaces. Built-in APIs, observability, and chargeback systems provide complete transparency for CPU, GPU, storage, and network usage.
Dynamic GPU Fabric
Ultra-low-latency GPU pooling enables any VM or container to attach 1–30+ GPUs, even across physical hosts. Perfect for multi-GPU training, distributed inference, or parallel experiments without hardware constraints.
Software-Defined Memory
Pooled RAM exposed via high-speed interconnects and RDMA allows workloads to utilise multi-terabyte memory as if local. Ideal for large language models and in-memory vector databases.
Why whitesky.cloud AI is Different
Disaggregated Yet Unified
Compute, GPUs, and memory orchestrated as one seamless cloud platform, enabling right-sizing of every workload without compromise.
Burst Without Waste
Compose "impossible" machines for intensive sprints—combining many GPUs with huge RAM—then instantly return resources to the shared pool.
Governance by Design
Centralised IAM/RBAC, encryption, comprehensive audit trails, quotas, and guardrails across all tenants and workloads.
Predictable Economics
Built-in metering and chargeback systems keep GPU-hours optimised and spending transparent across all departments and projects.
Industry Use Cases
Financial Services
Sub-10ms inference near exchanges for algorithmic trading. Burst risk calculation runs with pooled GPUs during market volatility whilst maintaining regulatory compliance.
Healthcare
On-premises imaging AI ensures PHI remains secure. Pooled memory architecture supports huge medical imaging models whilst maintaining GDPR compliance.
Manufacturing
Edge quality assurance with sub-50ms response times. Central re-training with 16–30 GPUs overnight for continuous improvement of production models.
Public Sector
Air-gapped, in-country data sovereignty with full NIS2 and GDPR alignment. Maintain complete control over sensitive government and citizen data.
Compliance & Governance
whitesky.cloud ensures complete GDPR and NIS2 compliance whilst protecting intellectual property. Your data and models remain under your policies with unified IAM/RBAC, comprehensive encryption, and detailed audit logging across all operations.
Request Our Comprehensive Whitepaper
What You'll Learn
- How GPU and memory pooling transforms AI infrastructure economics
- Technical architecture for sovereign, high-performance AI deployment
- Compliance strategies for GDPR, NIS2, and industry-specific regulations
- Real-world case studies from finance, healthcare, and manufacturing
- Implementation roadmap for migrating from traditional infrastructure
"Get instant access to our detailed technical guide covering composable AI infrastructure, resource pooling strategies, and compliance frameworks for enterprise deployments."
Frequently Asked Questions
How does latency compare between on-premises, edge, and hybrid deployments?
whitesky.cloud delivers single-digit millisecond latency for on-premises deployments, sub-50ms for edge computing, and flexible hybrid configurations that balance performance with geographic distribution requirements.
What data residency and sovereignty guarantees do you provide?
Complete data sovereignty with GDPR and NIS2 compliance. Your data never leaves your defined boundaries, with comprehensive audit trails and encryption both at rest and in transit.
How do GPU and memory pooling work for both training and inference?
Dynamic resource allocation allows training jobs to burst across multiple GPUs whilst inference workloads can access pooled memory for large models. Resources automatically return to the pool when jobs complete.
Does whitesky.cloud integrate with our existing Kubernetes and S3 tools?
Full compatibility with existing Kubernetes deployments, S3-compatible object storage APIs, and seamless integration with current IAM/SSO systems through standard protocols.
What support, SLA, and observability options are available?
Enterprise-grade SLAs with 24/7 support, comprehensive observability dashboards, automated chargeback systems, and detailed performance monitoring across all resource pools.