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Multi-Cloud Architecture Patterns: AWS vs Azure vs GCP

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Winnoventures Cloud TeamApril 20, 20258 min read

Why Multi-Cloud?

Vendor lock-in is a real risk. Here's how we architect systems that can run across AWS, Azure, and GCP.

Abstraction Layers

The key is abstracting cloud-specific services behind interfaces:

  • Storage: S3 / Azure Blob / GCS → abstract as "ObjectStorage"
  • Queues: SQS / Service Bus / Pub/Sub → abstract as "MessageQueue"
  • Functions: Lambda / Azure Functions / Cloud Functions → abstract as "Serverless"

    • Tools like Terraform and Pulumi help here — same infrastructure code, multiple providers.

    When to Use Each Cloud

    AWS — Best ecosystem, most services, largest talent pool. Default choice.

    Azure — Best for Microsoft-heavy enterprises (Active Directory, Office 365 integration).

    GCP — Best for data and AI workloads (BigQuery, Vertex AI are class-leading).

    Kubernetes as the Portability Layer

    Kubernetes is our multi-cloud portability layer. EKS, AKS, and GKE all run the same workloads. We use ArgoCD for GitOps across all clusters.

    Cost Comparison

    For a typical mid-size web application (1000 req/s):

  • AWS: ~$2,400/month
  • Azure: ~$2,600/month
  • GCP: ~$2,100/month

    • GCP often wins on raw compute costs but AWS wins on total ecosystem value.

    Conclusion

    Multi-cloud adds complexity. Only adopt it if vendor lock-in is a genuine business risk or you need geo-specific compliance.

    AWSAzureGCPCloudArchitecture
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    Winnoventures Cloud Team

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