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