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

• 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

Related Skills

• terraform-module-library

• For IaC implementation

• cost-optimization

• For cost management

• hybrid-cloud-networking

• For connectivity