multi-cloud-architecture

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Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes.

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.

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

Service rows group broad use cases, not drop-in equivalents. Azure SQL Database is not PostgreSQL/MySQL, Event Hubs Kafka compatibility is not the complete Kafka feature set, and S3-compatible APIs vary. Verify current service availability, regional/compliance constraints and actual application contracts. Multi-cloud replication needs explicit consistency/conflict, RPO/RTO, identity and failback design; geographic placement alone does not establish data sovereignty compliance. Measure dual-run operations, networking/egress, backups and team costs before committing; the 30–70% savings below is an illustrative historical range, not a quote or recommendation to buy commitments.

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

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.