multi-cloud-architecture

wshobson's avatarfrom wshobson

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.

⭐24.2kstars🔀2.7kforks📁View on GitHub🕐Updated Jan 1, 2026
[Cloud Infrastructure]

When & Why to Use This Skill

This Claude skill provides a comprehensive decision framework for designing and implementing multi-cloud architectures across AWS, Azure, and GCP. It enables architects to build cloud-agnostic systems, avoid vendor lock-in, and leverage best-of-breed services through detailed service comparisons, architectural patterns, and cost optimization strategies.

Use Cases

  • Multi-Cloud Strategy Development: Creating robust frameworks to distribute workloads across AWS, Azure, and GCP to enhance resilience and flexibility.
  • Cloud Service Comparison & Selection: Evaluating and mapping equivalent services (Compute, Storage, Database) across different providers to find the optimal fit for specific technical needs.
  • Disaster Recovery Planning: Designing cross-cloud failover architectures where a secondary cloud provider serves as a recovery site to ensure business continuity.
  • Cost Optimization & Management: Implementing strategies like reserved instances and spot pricing across multiple providers to minimize global infrastructure spend.
  • Cloud-Agnostic Implementation: Utilizing abstraction layers like Kubernetes and Terraform to build portable applications that run seamlessly on any major cloud platform.
  • Phased Cloud Migration: Planning and executing low-risk migrations between cloud providers using a structured four-phase approach (Assessment, Pilot, Migration, Optimization).
namemulti-cloud-architecture
descriptionDesign 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.

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

  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