Multi-Cloud Architecture
Decision framework and patterns for architecting applications across AWS, Azure, and GCP.
Purpose
Design cloud-agnostic architectures and make informed decisions about service selection across cloud providers.
When to Use Design multi-cloud strategies Migrate between cloud providers Select cloud services for specific workloads Implement cloud-agnostic architectures Optimize costs across providers Cloud Service Comparison Compute Services AWS Azure GCP Use Case EC2 Virtual Machines Compute Engine IaaS VMs ECS Container Instances Cloud Run Containers EKS AKS GKE Kubernetes Lambda Functions Cloud Functions Serverless Fargate Container Apps Cloud Run Managed containers Storage Services AWS Azure GCP Use Case S3 Blob Storage Cloud Storage Object storage EBS Managed Disks Persistent Disk Block storage EFS Azure Files Filestore File storage Glacier Archive Storage Archive Storage Cold storage Database Services AWS Azure GCP Use Case RDS SQL Database Cloud SQL Managed SQL DynamoDB Cosmos DB Firestore NoSQL Aurora PostgreSQL/MySQL Cloud Spanner Distributed SQL ElastiCache Cache for Redis Memorystore Caching
Reference: See references/service-comparison.md for complete comparison
Multi-Cloud Patterns Pattern 1: Single Provider with DR Primary workload in one cloud Disaster recovery in another Database replication across clouds Automated failover Pattern 2: Best-of-Breed Use best service from each provider AI/ML on GCP Enterprise apps on Azure General compute on AWS Pattern 3: Geographic Distribution Serve users from nearest cloud region Data sovereignty compliance Global load balancing Regional failover Pattern 4: Cloud-Agnostic Abstraction Kubernetes for compute PostgreSQL for database S3-compatible storage (MinIO) Open source tools Cloud-Agnostic Architecture Use Cloud-Native Alternatives Compute: Kubernetes (EKS/AKS/GKE) Database: PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL) Message Queue: Apache Kafka (MSK/Event Hubs/Confluent) Cache: Redis (ElastiCache/Azure Cache/Memorystore) Object Storage: S3-compatible API Monitoring: Prometheus/Grafana Service Mesh: Istio/Linkerd Abstraction Layers Application Layer ↓ Infrastructure Abstraction (Terraform) ↓ Cloud Provider APIs ↓ AWS / Azure / GCP
Cost Comparison Compute Pricing Factors AWS: On-demand, Reserved, Spot, Savings Plans Azure: Pay-as-you-go, Reserved, Spot GCP: On-demand, Committed use, Preemptible Cost Optimization Strategies Use reserved/committed capacity (30-70% savings) Leverage spot/preemptible instances Right-size resources Use serverless for variable workloads Optimize data transfer costs Implement lifecycle policies Use cost allocation tags Monitor with cloud cost tools
Reference: See references/multi-cloud-patterns.md
Migration Strategy Phase 1: Assessment Inventory current infrastructure Identify dependencies Assess cloud compatibility Estimate costs Phase 2: Pilot Select pilot workload Implement in target cloud Test thoroughly Document learnings Phase 3: Migration Migrate workloads incrementally Maintain dual-run period Monitor performance Validate functionality Phase 4: Optimization Right-size resources Implement cloud-native services Optimize costs Enhance security Best Practices Use infrastructure as code (Terraform/OpenTofu) Implement CI/CD pipelines for deployments Design for failure across clouds Use managed services when possible Implement comprehensive monitoring Automate cost optimization Follow security best practices Document cloud-specific configurations Test disaster recovery procedures Train teams on multiple clouds Reference Files references/service-comparison.md - Complete service comparison references/multi-cloud-patterns.md - Architecture patterns Related Skills terraform-module-library - For IaC implementation cost-optimization - For cost management hybrid-cloud-networking - For connectivity