multi-cloud-architecture

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

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

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

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AGI Framework Integration

Adapted for @techwavedev/​agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior deployment configurations, rollback procedures, and incident post-mortems. Avoid re-discovering infrastructure patterns.

# Check for prior infrastructure context before starting
python3 execution/​memory_manager.py auto --query "deployment configuration and patterns for Multi Cloud Architecture"

Storing Results

After completing work, store infrastructure decisions for future sessions:

python3 execution/​memory_manager.py store \
  --content "Deployment pipeline: configured blue-green deployment with health checks on port 8080" \
  --type technical --project <project> \
  --tags multi-cloud-architecture devops

Multi-Agent Collaboration

Broadcast deployment changes so frontend and backend agents update their configurations accordingly.

python3 execution/​cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Deployed infrastructure changes — updated CI/​CD pipeline with new health check endpoints" \
  --project <project>

Playbook Integration

Use the ship-saas-mvp or full-stack-deploy playbook to sequence this skill with testing, documentation, and deployment verification.

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