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multi-cloud-architecture

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.

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name:multi-cloud-architecturedescription: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

    AWSAzureGCPUse Case
    EC2Virtual MachinesCompute EngineIaaS VMs
    ECSContainer InstancesCloud RunContainers
    EKSAKSGKEKubernetes
    LambdaFunctionsCloud FunctionsServerless
    FargateContainer AppsCloud RunManaged containers

    Storage Services

    AWSAzureGCPUse Case
    S3Blob StorageCloud StorageObject storage
    EBSManaged DisksPersistent DiskBlock storage
    EFSAzure FilesFilestoreFile storage
    GlacierArchive StorageArchive StorageCold storage

    Database Services

    AWSAzureGCPUse Case
    RDSSQL DatabaseCloud SQLManaged SQL
    DynamoDBCosmos DBFirestoreNoSQL
    AuroraPostgreSQL/MySQLCloud SpannerDistributed SQL
    ElastiCacheCache for RedisMemorystoreCaching

    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 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