Cloud AI Without the Exposure
Public AI APIs send your data to shared infrastructure you do not control. Private cloud deployment gives you the scalability of AWS, Azure, or GCP while keeping every model, prompt, and response inside your own Virtual Private Cloud. No shared tenancy, no data co-mingling, no compliance headaches. Your AI runs on dedicated instances within your cloud account, governed by your security policies.
VPC Isolation
Models deploy inside your existing VPC with private subnets, no public endpoints, and VPC peering to your application layer. Network traffic never touches the public internet.
Dedicated GPU Instances
Reserved p4d, p5, or g5 instances on AWS. NC-series or ND-series on Azure. A3 or G2 instances on GCP. Your workloads run on hardware no other tenant touches.
Compliance Boundary Control
Data residency in specific regions. Encryption keys in your own AWS KMS, Azure Key Vault, or GCP Cloud KMS. IAM policies that satisfy SOC 2, HIPAA, and FedRAMP auditors.
Auto-Scaling Infrastructure
Scale GPU instances up during peak hours and down overnight. Spot instances for batch workloads cut costs by 60-70%. Reserved instances for baseline capacity with on-demand burst.
Private Cloud Deployment Process
Architecture
VPC design and network topology
Provision
GPU instances and storage
Deploy
Model serving infrastructure
Secure
IAM, encryption, audit logs
Optimize
Cost and performance tuning
Architecture
VPC design and network topology
Provision
GPU instances and storage
Deploy
Model serving infrastructure
Secure
IAM, encryption, audit logs
Optimize
Cost and performance tuning
Private Cloud AI Platform
Cloud Provider Architecture
Each cloud provider has different GPU instance families, networking capabilities, and pricing models. We design the architecture around your existing cloud footprint rather than forcing a provider switch.
AWS private AI. Deploy on p5.48xlarge (8x H100) or p4d.24xlarge (8x A100) instances within your VPC. Use Amazon EFS or FSx for Lustre for model weight storage. SageMaker endpoints with VPC configuration for managed inference, or self-managed vLLM on EKS for full control. PrivateLink endpoints keep all traffic off the public internet.
Azure private AI. ND96amsr (8x A100) or NC A100 v4 instances in your Azure VNet. Azure AI Service with data residency guarantees, or self-hosted models on AKS with GPU node pools. Private Endpoints and Azure Firewall ensure network isolation. Managed Identity for authentication without credential rotation.
GCP private AI. A3 High (8x H100) or G2 (L4) instances in your VPC. Vertex AI with VPC Service Controls for managed inference, or GKE Autopilot with GPU node pools for self-managed deployments. Private Google Access keeps traffic within Google's network backbone.
Cost Optimization Strategies
Private cloud AI does not have to mean expensive cloud AI. The combination of reserved instances, spot capacity, and right-sizing reduces costs to a fraction of on-demand pricing.
Reserved capacity for baseline. Commit to 1-year or 3-year reserved instances for your steady-state workload. Savings of 40-60% compared to on-demand. We model your usage patterns to determine the right reservation level.
Spot instances for batch. Document processing, embedding generation, and offline analysis run on spot instances at 60-70% discount. Checkpointing ensures no work is lost when instances are reclaimed.
Scheduled scaling. Scale down to minimum capacity outside business hours. Most enterprises see 70% of AI usage during a 10-hour window. Automated scaling policies match capacity to demand without manual intervention.
Who This Is For
Private cloud AI is ideal for organizations that need the scalability of cloud infrastructure but cannot use shared AI services. If your compliance framework requires data residency, dedicated compute, and audit trails for AI workloads, this is the architecture that checks every box without the capital expenditure of on-premises hardware.
Contact us at ben@oakenai.tech