multi-cloud-architecture✓ Pass

SKILL.md Content

---
name: multi-cloud-architecture
description: 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.
---

# Multi-Cloud Architecture

Decision framework and patterns for architecting applications across AWS, Azure, and GCP.

## Do not use this skill when

- The task is unrelated to multi-cloud architecture
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Purpose

Design cloud-agnostic architectures and make informed decisions about service selection across cloud providers.

## Use this skill when

- 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

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

License

Declared license: MIT

MIT License

Copyright (c) 2026 rmyndharis

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
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SOFTWARE.

View the license in the source repository — the version published there is authoritative.