Multi Cloud Architecture
Gives you service mapping tables across AWS, Azure, GCP, and OCI so you can pick equivalent compute, storage, and database services without hunting through docs. Includes migration phases, cost comparison frameworks, and four concrete multi-cloud patterns like best-of-breed selection and geographic distribution. The cloud-agnostic architecture section is solid for avoiding vendor lock-in with Kubernetes and open source alternatives. Most useful when you're planning a multi-cloud strategy or need to justify service choices across providers. The service comparison tables alone save hours of research.
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- last updated
- Aug 21, 2026
Install
$ npx -y skills add wshobson/agents --skill multi-cloud-architecture --agent claude-codeInstalls into .claude/skills of the current project.
Files
Multi-Cloud Architecture
Decision framework and patterns for architecting applications across AWS, Azure, GCP, and OCI.
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 | OCI | Use Case |
|---|---|---|---|---|
| EC2 | Virtual Machines | Compute Engine | Compute | IaaS VMs |
| ECS | Container Instances | Cloud Run | Container Instances | Containers |
| EKS | AKS | GKE | OKE | Kubernetes |
| Lambda | Functions | Cloud Functions | Functions | Serverless |
| Fargate | Container Apps | Cloud Run | Container Instances | Managed containers |
Storage Services
| AWS | Azure | GCP | OCI | Use Case |
|---|---|---|---|---|
| S3 | Blob Storage | Cloud Storage | Object Storage | Object storage |
| EBS | Managed Disks | Persistent Disk | Block Volumes | Block storage |
| EFS | Azure Files | Filestore | File Storage | File storage |
| Glacier | Archive Storage | Archive Storage | Archive Storage | Cold storage |
Database Services
| AWS | Azure | GCP | OCI | Use Case |
|---|---|---|---|---|
| RDS | SQL Database | Cloud SQL | MySQL HeatWave | Managed SQL |
| DynamoDB | Cosmos DB | Firestore | NoSQL Database | NoSQL |
| Aurora | PostgreSQL/MySQL | Cloud Spanner | Autonomous Database | Distributed SQL |
| ElastiCache | Cache for Redis | Memorystore | OCI Cache | 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
- Regulated data platforms on OCI
- 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/OKE)
- Database: PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL/MySQL HeatWave)
- Message Queue: Apache Kafka or managed streaming (MSK/Event Hubs/Confluent/OCI Streaming)
- Cache: Redis (ElastiCache/Azure Cache/Memorystore/OCI Cache)
- 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 / OCI
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
- OCI: Pay-as-you-go, annual commitments, burstable/flexible shapes, preemptible instances
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
Related Skills
terraform-module-library- For IaC implementationcost-optimization- For cost managementhybrid-cloud-networking- For connectivity
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