multi-cloud-architecture
upstream
backend

Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.

ID: multi-cloud-architecture
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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

Ôåô

Infrastructure Abstraction (Terraform)

Ôåô

Cloud Provider APIs

Ôåô

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


1. Use reserved/committed capacity (30-70% savings)

2. Leverage spot/preemptible instances

3. Right-size resources

4. Use serverless for variable workloads

5. Optimize data transfer costs

6. Implement lifecycle policies

7. Use cost allocation tags

8. 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


1. Use infrastructure as code (Terraform/OpenTofu)

2. Implement CI/CD pipelines for deployments

3. Design for failure across clouds

4. Use managed services when possible

5. Implement comprehensive monitoring

6. Automate cost optimization

7. Follow security best practices

8. Document cloud-specific configurations

9. Test disaster recovery procedures

10. 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

Informacion

Estado
Activo
Origen upstream
Usos 3
Ultimo uso 23/01 16:57
Actualizado 09/01/2026 02:14

Archivo origen

Z:\Repositorios\core\upstream\plugins\cloud-infrastructure\skills\multi-cloud-architecture\SKILL.md