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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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@sickn33

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Multi-Cloud Architecture Design Assistant

Skill Overview


Multi-Cloud Architecture provides a cross-AWS, Azure, and GCP multi-cloud architecture design decision framework to help you choose and integrate the best cloud services, avoid vendor lock-in, and achieve a truly cloud-agnostic architecture.

Use Cases

1. Multi-Cloud Architecture Design and Planning


When an enterprise needs to use multiple cloud platforms simultaneously to spread risk, optimize costs, or leverage the strengths of each platform, this skill offers a complete architecture design methodology. It includes four core patterns: single-cloud with disaster recovery in another region, best-practice combinations that take advantage of what each platform does best, geographically distributed deployments, and design of a fully cloud-agnostic abstraction layer.

2. Cloud Service Selection and Comparison


Faced with the many services offered by the three major cloud platforms—AWS, Azure, and GCP—this skill provides detailed comparison tables for compute, storage, and database services, helping you select the most suitable cloud services based on your specific workloads. Whether it’s IaaS virtual machines, container orchestration, Kubernetes, or serverless computing, you can find corresponding cross-cloud equivalents.

3. Cloud Migration and Cost Optimization


When you need to migrate existing workloads from one cloud platform to another, or want to optimize the cost of cross-cloud deployments, this skill provides a four-phase migration strategy (assessment, pilot, migration, and optimization) and practical cost-optimization tips, including recommendations such as reserved instances, Spot instances, and resource optimization.

Core Features

1. Cloud Service Comparison and Decision Framework


Provides one-stop comparison tables across AWS, Azure, and GCP for areas such as compute, storage, and databases. Coverage ranges from IaaS services like EC2/Virtual Machines/Compute Engine to serverless options such as Lambda/Functions/Cloud Functions, helping you quickly identify the cloud service combinations that fit your business needs.

2. Multi-Cloud Architecture Pattern Design


Supports four mainstream multi-cloud architecture patterns:
  • Single-cloud with disaster recovery in another region: the primary cloud hosts the business workload, while the secondary cloud provides disaster recovery capabilities

  • Best-practice combinations: use GCP for AI/ML, Azure for enterprise applications, and AWS for general computing

  • Geographically distributed deployment: connect users nearby to meet data sovereignty requirements

  • Cloud-agnostic abstraction layer: use tools such as Kubernetes and Terraform to enable unified cross-cloud management

    • 3. Cloud-Agnostic Architecture and Best Practices


    Guides you to build a truly cloud-agnostic architecture using open-source and standardized tools, including a unified compute layer with Kubernetes (EKS/AKS/GKE), a unified storage layer with S3-compatible APIs, unified monitoring with Prometheus/Grafana, and infrastructure-as-code practices with Terraform/OpenTofu—fundamentally reducing dependence on any specific cloud provider.

    Frequently Asked Questions

    What’s the difference between a multi-cloud architecture and a single-cloud architecture?


    A single-cloud architecture deploys all workloads on one cloud platform: it’s simpler to manage but carries the risk of vendor lock-in. A multi-cloud architecture uses two or more cloud platforms: it can spread risk, optimize costs, and leverage each platform’s strengths, but requires more complex operations and cross-cloud management capabilities. A common compromise is to use a cloud-agnostic technology stack (such as Kubernetes) to reduce cross-cloud management complexity.

    How can I avoid cloud vendor lock-in?


    The key to avoiding cloud vendor lock-in is to adopt a cloud-agnostic abstraction layer: use Kubernetes as a unified compute platform, replace managed databases with open-source databases such as PostgreSQL/MySQL, use S3-compatible storage interfaces, and manage infrastructure as code with Terraform/OpenTofu. This way, when needed, you can relatively smoothly migrate workloads from one cloud platform to another.

    Which is better for my business: AWS, Azure, or GCP?


    There is no absolute “best” choice—it depends on your specific requirements. If you need mature enterprise-grade services and global coverage, AWS is a solid option. If your organization is already using the Microsoft ecosystem (such as Office 365 and Active Directory), Azure has higher integration. If you value AI/ML capabilities or prefer an open-source-friendly environment, GCP has an advantage. The service comparison tables and decision framework provided by this skill can help you choose based on your specific workloads.