🎯

multi-cloud-architecture

🎯Skill

A planning guide for running an app across more than one cloud company, such as AWS, Azure, Google Cloud and Oracle Cloud. It helps you pick matching services, avoid being tied to one company, and keep costs down.

wshobson/agents8,275 Installs27,673Added Jan 31, 2026

Installation

$npx skills add wshobson/agents --skill multi-cloud-architecture

Run it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).

Uses skills.sh · Node.js 22.20+

After installing

Ask for related work and the agent uses it on its own, or call it yourself.

Run it (Claude Code)/multi-cloud-architecture

Install by hand

Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.

Open the skill folder →

This project .claude/skills/multi-cloud-architecture/
All projects ~/.claude/skills/multi-cloud-architecture/

These paths are for Claude Code. For other tools, the command above puts it in the right place.

📦Same repositorywshobson/agents (268 items)

What this skill does

Overview

This skill helps you plan systems that use several cloud providers at once. A cloud provider is a company that rents out servers, storage and databases over the internet. The skill compares services across AWS, Azure, GCP and OCI and gives clear patterns to follow.

Key Features

Compare matching compute, storage and database services across the four providers in side-by-side tables.
Pick from four setups, such as keeping a backup copy in a second cloud in case of disaster.
Build a setup that moves between clouds easily using shared tools like Kubernetes (runs apps in containers) and Terraform (sets up servers from code files).
Cut costs with tips such as reserving capacity ahead of time and using cheaper short-term servers.
Follow a four-phase plan to move workloads from one cloud to another.

Who is this for?

It suits engineers and teams who design cloud systems or plan a move between providers. It also helps anyone who wants to avoid depending on a single company.

Summarized by AI from this skill's SKILL.md. Suggest a better one

SKILL.md

View on GitHub →

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

Multi-Cloud Architecture

Decision framework and patterns for architecting applications across AWS, Azure, GCP, and OCI.

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	OCI	Use Case
EC2	Virtual Machines	Compute Engine	Compute	IaaS VMs
ECS	Container Instances	Cloud Run	Container Instances	Containers
EKS	AKS	GKE	OKE	Kubernetes
Lambda	Functions	Cloud Functions	Functions	Serverless
Fargate	Container Apps	Cloud Run	Container Instances	Managed containers

Storage Services

AWS	Azure	GCP	OCI	Use Case
S3	Blob Storage	Cloud Storage	Object Storage	Object storage
EBS	Managed Disks	Persistent Disk	Block Volumes	Block storage
EFS	Azure Files	Filestore	File Storage	File storage
Glacier	Archive Storage	Archive Storage	Archive Storage	Cold storage

Database Services

AWS	Azure	GCP	OCI	Use Case
RDS	SQL Database	Cloud SQL	MySQL HeatWave	Managed SQL
DynamoDB	Cosmos DB	Firestore	NoSQL Database	NoSQL
Aurora	PostgreSQL/MySQL	Cloud Spanner	Autonomous Database	Distributed SQL
ElastiCache	Cache for Redis	Memorystore	OCI Cache	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
Regulated data platforms on OCI
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/OKE)
Database: PostgreSQL/MySQL (RDS/SQL Database/Cloud SQL/MySQL HeatWave)
Message Queue: Apache Kafka or managed streaming (MSK/Event Hubs/Confluent/OCI Streaming)
Cache: Redis (ElastiCache/Azure Cache/Memorystore/OCI Cache)
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 / OCI

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
OCI: Pay-as-you-go, annual commitments, burstable/flexible shapes, preemptible instances

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

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