Multi cloud architecture

Skill ComeOnOliver/skillshub/skills/aiskillstore/marketplace/wshobson/multi-cloud-architecture

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npx -y skills add ComeOnOliver/skillshub --skill multi-cloud-architecture

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Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, and GCP. Use when building multi-cloud systems, avoiding vendor lock-in, or leveraging best-of-breed services from multiple providers.

SKILL.md

4.7 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it

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

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

What ships with it

10.4 KB alongside SKILL.md

GitHub clipped this repository’s file list, so this is at least 1 file and may be more.

skill-report.json10.4 KB

Gives 0 of the 12 instructions most architecture codebase skills give in ~1.1k tokens

Counted across 811 of the 1,134 authors here whose files we hold, read 2026-08-07

Ask the user which candidate to explorein 45 of 811, across 15 files
Apply the deletion test to suspected shallow modulesin 43 of 811, across 15 files
Read any relevant architecture decision records firstin 31 of 811, across 8 files
Use exact glossary terms in every suggestionin 30 of 811, across 10 files
Accept dependencies instead of creating themin 24 of 811, across 5 files
Include before and after visualisations for each candidatein 24 of 811, across 5 files
Read the domain glossary before exploringin 24 of 811, across 6 files
Return results instead of producing side effectsin 23 of 811, across 4 files
Explore the codebase for shallow modules and frictionin 23 of 811, across 3 files
Introduce seams only where things varyin 22 of 811, across 3 files
Reduce the number of methodsin 21 of 811, across 2 files
Design deep modules with small interfacesin 21 of 811, across 3 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.