Tsh Designing Multi Cloud Architecture

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

Storage Services

Database Services

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

Process

1. Assess requirements → Identify drivers (DR, compliance, best-of-breed, cost)
2. Choose pattern → Select from patterns above based on requirements
3. Map services → Use service comparison tables to select equivalents
4. Design abstraction → Use Kubernetes + Terraform for portability
5. Plan networking → Design cross-cloud connectivity (VPN, interconnect)
6. Implement IaC → Use tsh-implementing-terraform-modules skill
7. Set up observability → Unified monitoring across clouds
8. Document runbooks → Cloud-specific operational procedures

Checklist

• Multi-cloud drivers documented (DR, compliance, vendor diversification)
• Service equivalents mapped across target clouds
• Abstraction layer chosen (Kubernetes, Terraform, or both)
• Cross-cloud networking designed and secured
• Identity federation configured (single IAM source of truth)
• Unified monitoring and logging implemented
• Cost allocation and tracking across clouds
• DR/failover procedures tested
• Team trained on all target cloud platforms

Anti-Patterns

Reference Files

• references/service-comparison.md - Complete service comparison
• references/multi-cloud-patterns.md - Architecture patterns

Related Skills

• tsh-implementing-terraform-modules - For IaC implementation
• tsh-optimizing-cloud-cost - For cost management