Cloud Platform Infrastructure: Architectures and How They Operate on AWS, Azure, and GCP
Cloud platform infrastructure is the foundation that delivers compute, storage, networking, identity, and management services as scalable, on-demand resources. Different architectural patterns fit different workloads and business goals. Below are common infrastructure architectures, how they operate, and how they map to Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
1. Traditional Lift-and-Shift (Rehost)
- Description: Move existing on-premises VMs and applications to the cloud with minimal changes.
- How it operates: Provision virtual machines, attach storage, configure networking and security groups; use cloud-native tooling for monitoring and backups.
- AWS: EC2 instances, EBS for block storage, VPC for networking, IAM for identity, CloudWatch for monitoring.
- Azure: Azure Virtual Machines, Managed Disks, Virtual Network (VNet), Azure AD/Role-Based Access Control (RBAC), Azure Monitor.
- GCP: Compute Engine VMs, Persistent Disks, VPC, Cloud IAM, Cloud Monitoring.
2. Cloud-Native (Refactor / Replatform)
- Description: Re-architect applications to use managed platform services, microservices, containers, or serverless to achieve scalability and operability.
- How it operates: Replace self-managed components with managed services (databases, queues), deploy containers or serverless functions, use service meshes and CI/CD.
- AWS: ECS/EKS for containers, Lambda for serverless, RDS/Aurora for managed databases, SQS/Kinesis for messaging, App Mesh.
- Azure: Azure Kubernetes Service (AKS), Azure Functions, Azure SQL/Managed Instances, Service Bus/Event Grid, Azure Service Fabric.
- GCP: Google Kubernetes Engine (GKE), Cloud Functions / Cloud Run, Cloud SQL / Spanner, Pub/Sub, Istio integrations.
3. Microservices and Containerized Architectures
- Description: Decompose monoliths into small, independently deployable services running in containers; often paired with orchestration and CI/CD.
- How it operates: Use container registries, orchestrators, ingress/load balancing, autoscaling, and observability stacks.
- AWS: EKS (Kubernetes), ECR (container registry), ALB/NLB, Cluster Autoscaler, CloudWatch/Prometheus.
- Azure: AKS, Azure Container Registry (ACR), Azure Load Balancer/Application Gateway, Virtual Node, Azure Monitor.
- GCP: GKE, Container Registry / Artifact Registry, Cloud Load Balancing, Horizontal Pod Autoscaler, Cloud Monitoring/Logging.
4. Serverless Architectures
- Description: Run code without managing servers; autoscale to zero, billed per execution.
- How it operates: Event-driven functions, managed triggers, managed APIs, and backend services.
- AWS: Lambda functions, API Gateway, EventBridge, Step Functions.
- Azure: Azure Functions, API Management, Event Grid, Durable Functions.
- GCP: Cloud Functions, API Gateway / Cloud Endpoints, Eventarc, Workflows.
5. Hybrid Cloud and Multi-Cloud Architectures
- Description: Combine on-premises infrastructure with public cloud(s) or span multiple clouds for redundancy, compliance, or vendor flexibility.
- How it operates: Use secure networking (VPN/DirectConnect/ExpressRoute), identity federation, consistent management/control planes, and data synchronization tools.
- AWS: AWS Outposts, Direct Connect, Transit Gateway, IAM + AD integration, AWS Systems Manager.
- Azure: Azure Arc, ExpressRoute, Virtual WAN, Azure AD hybrid identity, Azure Stack.
- GCP: Anthos, Dedicated Interconnect, VPC peering, Cloud Identity, Migrate for Compute Engine.
6. High-Performance & Compute-Intensive Architectures
- Description: Architectures optimized for HPC, batch processing, ML training, or GPU workloads.
- How it operates: Use specialized instance types, fast networking, parallel storage, scheduling/orchestration.
- AWS: EC2 GPU/FPGA instances (P4, G5), FSx for Lustre, Batch, ParallelCluster, Elastic Fabric Adapter (EFA).
- Azure: ND/NC series VMs, Azure CycleCloud, Azure Batch, Azure NetApp Files.
- GCP: A2 GPU instances, Cloud TPU, Filestore, Batch, Sole-tenant nodes.
7. Data-Driven and Analytics Architectures
- Description: Centralized or distributed data platforms for analytics, data lakes, and real-time processing.
- How it operates: Ingest, store, process, and analyze data using managed services, data warehouses, streaming platforms, and governance tools.
- AWS: S3 data lakes, Glue/EMR for ETL, Redshift for warehousing, Kinesis for streaming, Lake Formation.
- Azure: Azure Data Lake Storage, Data Factory, Synapse Analytics, Event Hubs, Databricks integration.
- GCP: Cloud Storage, Dataflow for stream/batch, BigQuery for analytics, Pub/Sub for messaging, Dataproc/Dataprep.
8. Secure, Compliant Architectures
- Description: Architect with security and compliance baked in—network segmentation, encryption, least privilege, logging, and auditability.
- How it operates: Use dedicated security services, encryption at rest/in transit, centralized logging and SIEM, automated compliance checks.
- AWS: VPC Service Controls, KMS, AWS Config, GuardDuty, Security Hub, IAM policies.
- Azure: Azure Policy, Key Vault, Defender for Cloud, Azure Sentinel, RBAC.
- GCP: VPC Service Controls, Cloud KMS, Security Command Center, Cloud Audit Logs, IAM.
Common Operational Components Across Providers
- Networking: Virtual private networks, subnets, routing, load balancers, and peering.
- Compute: VM instances, autoscaling groups, container orchestration, serverless functions.
- Storage: Block, object, and file storage with tiers for performance and cost.
- Identity & Access: Centralized identity, roles, and policies for least-privilege access.
- Monitoring & Logging: Metrics, traces, logs, alerting, and dashboards.
- Automation & IaC: Infrastructure as Code (Terraform, CloudFormation, ARM templates, Deployment Manager) for repeatable deployments.
Designing the Right Infrastructure
- Choose architecture by workload: transactional apps (managed databases, autoscaling VMs), analytics (data lake + warehouse), web/mobile (serverless + CDN).
- Optimize for cost: right-size instances, use reserved/committed pricing, autoscaling, and storage tiers.
- Plan for resilience: multi-AZ/region deployment, backups, and disaster recovery runbooks.
- Prioritize security: network segmentation, encryption, identity management, and continuous compliance checks.
- Use managed services where maturity and control trade-offs favor operational simplicity.
For a tailored infrastructure blueprint and cost estimate across AWS, Azure, or GCP, request a cloud assessment and architecture design engagement.
