techwavedev 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
- 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
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.
# Check for prior infrastructure context before starting
python3 execution/memory_manager.py auto --query "deployment configuration and patterns for Multi Cloud Architecture"
Storing Results
After completing work, store infrastructure decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
--type technical --project <project> \
--tags multi-cloud-architecture devops
Multi-Agent Collaboration
Broadcast deployment changes so frontend and backend agents update their configurations accordingly.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Deployed infrastructure changes — updated CI/CD pipeline with new health check endpoints" \
--project <project>
Playbook Integration
Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.