Hybrid & Multi-Cloud AI Architecture
Cloud GPU, On-Prem and Hybrid AI Workloads
Combining the scalability of public cloud with the control of private GPU clusters — ideal for hybrid AI systems, burst workloads, and regulated workloads.
Book a Hybrid AI Architecture WorkshopCloud GPU & Cloud-AI Compute
AWS EC2 GPU Instances
G5, P4, future-proof options for training & inference
EKS/ECS Orchestration
Container orchestration for GPU workloads
GPU-Cloud Integration
Integration with GPU-cloud / on-prem backends
Hybrid Orchestration & Connectivity
Secure Networking
VPCs, Direct Connect, Transit Gateway, private links, VPNs
Multi-Environment Workload Routing
Dev/test on cloud, heavy training on-prem or GPU-cloud, inference burst scaling
Data Pipelines & Storage
S3 + high-throughput storage options + data lakes for training
AI-Optimised Cloud Infrastructure Design
IAM & Security
Identity, access control, encryption & secrets management
Data Governance
Compliance, data residency, regulatory requirements
Cost Management
Autoscaling design and cost optimization for GPU workloads
Use Cases
Burst Training Jobs
High GPU capacity without overprovisioning
Hybrid Deployments for Regulated Workloads
Finance, government, research with compliance requirements
Disaster Recovery & Geo-Redundancy
Across on-prem and cloud environments
Our Approach
We design cloud-AI architectures that are optimized for performance, cost, security and compliance.
Assess
Understand your AI workloads, data requirements and compliance needs
Design
Architect hybrid cloud + GPU infrastructure with secure connectivity
Deploy
Build and configure cloud environments with IaC and automation
Optimize
Monitor costs, performance and scale as workloads grow
Technology Stack
We work with AWS and leading cloud platforms to deliver production-ready AI infrastructure.
AWS Services
- • EC2 GPU instances (P4, P5)
- • EKS with GPU node groups
- • S3, FSx for Lustre
- • VPC, Direct Connect
- • IAM, KMS, Secrets Manager
Infrastructure as Code
- • Terraform for AWS
- • CloudFormation
- • Ansible automation
- • GitHub Actions CI/CD
- • GitOps workflows
Orchestration
- • Kubernetes + GPU Operator
- • Ray for distributed AI
- • Airflow for pipelines
- • MLflow tracking
- • Prometheus + Grafana
Why Hybrid AI Architecture?
Flexibility
Scale up and down depending on workload
Cost Efficiency
Use cloud when needed, rely on owned GPU infra when stable
Compliance & Sovereignty
Keep sensitive data on-prem while using cloud for non-sensitive workloads
Ready to Build Your Cloud-AI Architecture?
Talk to our cloud-AI team about your hybrid infrastructure requirements.