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

| AWS | Azure | GCP | Use Case |

|-----|-------|-----|----------|

| EKS | AKS | GKE | Kubernetes |

Storage Services

| AWS | Azure | GCP | Use Case |

|-----|-------|-----|----------|

Database Services

| AWS | Azure | GCP | Use Case |

|-----|-------|-----|----------|

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

multi-cloud-architecture

Documentation