multi-cloud-architecture
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SKILL.md
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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
↓
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 comparisonreferences/multi-cloud-patterns.md- Architecture patterns
Related Skills
terraform-module-library- For IaC implementationcost-optimization- For cost managementhybrid-cloud-networking- For connectivity
Source
git clone https://github.com/wshobson/agents/blob/main/plugins/cloud-infrastructure/skills/multi-cloud-architecture/SKILL.mdView on GitHub Overview
Multi-cloud architecture uses a decision framework and patterns to design applications that span AWS, Azure, and GCP. It helps select services across providers, avoid vendor lock-in, and leverage best-of-breed capabilities. The approach includes cloud-agnostic patterns, abstraction layers (Terraform), and cost-aware strategies.
How This Skill Works
Start with a workload classification, map to compute/storage/database options in each provider using the cloud service comparison, and apply cloud-agnostic patterns (Kubernetes, PostgreSQL, S3-compatible storage). Use an abstraction layer like Terraform to decouple application logic from provider APIs, and implement DR, data replication, and global distribution patterns.
When to Use It
- Design multi-cloud strategies
- Migrate between cloud providers
- Select cloud services for specific workloads
- Implement cloud-agnostic architectures
- Optimize costs across providers
Quick Start
- Step 1: Define workloads and requirements, then map them to compute/storage/database options across AWS, Azure, and GCP.
- Step 2: Compare services using the provided Compute/Storage/Database tables to select appropriate provider capabilities.
- Step 3: Implement an abstraction layer (Terraform) and establish DR, monitoring, and cost controls across cloud providers.
Best Practices
- Define a decision framework to map workloads to provider services based on requirements.
- Favor cloud-agnostic components where feasible (Kubernetes, PostgreSQL, MinIO-style storage, Redis caches).
- Use Terraform or similar IaC to abstract infrastructure across clouds.
- Use S3-compatible storage for portability and cross-cloud data strategies.
- Implement cross-cloud monitoring and cost controls; plan DR and regional failover.
Example Use Cases
- A SaaS app runs compute on AWS, enterprise workloads on Azure, and ML workloads on GCP to leverage each provider’s strengths.
- Disaster recovery across clouds with automated failover, cross-cloud DB replication, and IaC-managed migration paths.
- Global users served from nearest cloud region using geographic distribution and global load balancing across providers.
- Cloud-agnostic data plane using PostgreSQL across clouds and MinIO-compatible object storage for portability.
- Migration path from one provider to another by adopting best-of-breed components and cloud-agnostic abstractions.
Frequently Asked Questions
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