tomevault-io 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
Process
- Assess requirements → Identify drivers (DR, compliance, best-of-breed, cost)
- Choose pattern → Select from patterns above based on requirements
- Map services → Use service comparison tables to select equivalents
- Design abstraction → Use Kubernetes + Terraform for portability
- Plan networking → Design cross-cloud connectivity (VPN, interconnect)
- Implement IaC → Use
tsh-implementing-terraform-modulesskill - Set up observability → Unified monitoring across clouds
- Document runbooks → Cloud-specific operational procedures
Checklist
- Multi-cloud drivers documented (DR, compliance, vendor diversification)
- Service equivalents mapped across target clouds
- Abstraction layer chosen (Kubernetes, Terraform, or both)
- Cross-cloud networking designed and secured
- Identity federation configured (single IAM source of truth)
- Unified monitoring and logging implemented
- Cost allocation and tracking across clouds
- DR/failover procedures tested
- Team trained on all target cloud platforms
Anti-Patterns
| ❌ Don't | ✅ Do |
|---|---|
| Use proprietary services for portability needs | Choose cloud-agnostic alternatives (K8s, PostgreSQL) |
| Duplicate everything across clouds | Be intentional about what runs where |
| Manage each cloud differently | Use unified IaC and observability |
| Ignore data transfer costs | Design to minimize cross-cloud traffic |
Reference Files
references/service-comparison.md- Complete service comparisonreferences/multi-cloud-patterns.md- Architecture patterns
Related Skills
tsh-implementing-terraform-modules- For IaC implementationtsh-optimizing-cloud-cost- For cost management
Source: TheSoftwareHouse/copilot-collections — distributed by TomeVault.