multi-cloud-architecture

Use it when:: You're designing across more than one cloud, migrating between providers, or avoiding vendor lock-in.
It does:: Maps equivalent compute, storage and database services across AWS, Azure, GCP and OCI, and gives patterns for combining them.
You get:: A provider-neutral design and a service-selection decision, not provisioning; reference files hold the full comparison and patterns.
Patterns:: Single provider with cross-cloud DR, best-of-breed per provider, geographic distribution, and a cloud-agnostic abstraction on Kubernetes, Postgres, Kafka and S3-compatible storage.

TL;DR

A decision framework for multi-cloud architecture. The SKILL.md tabulates equivalent services across AWS, Azure, GCP and OCI for compute, storage and databases, then lays out four patterns: a primary cloud with disaster recovery in another, best-of-breed (AI on GCP, enterprise on Azure, regulated data on OCI, general compute on AWS), geographic distribution for latency and data sovereignty, and a cloud-agnostic abstraction built on Kubernetes, PostgreSQL/MySQL, Kafka, Redis, S3-compatible storage, Prometheus/Grafana and a service mesh behind a Terraform layer. It compares pricing models and lists cost-optimization strategies (reserved and spot capacity, right-sizing, serverless for variable load, lifecycle policies, allocation tags) and a four-phase migration (assess, pilot, migrate with dual-run, optimize). Best practices: infrastructure as code, CI/CD, design for cross-cloud failure, managed services, monitoring, and training teams on several clouds. Fuller comparison and pattern detail sit in reference files.

Summarizes the skill at 4bde316.

npx skills add https://github.com/wshobson/agents --skill multi-cloud-architecture

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First seen: Jan 20, 2026 on skills.sh

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Category	category:cloud category:engineering category:software-engineering Deploy, provision, observe and debug cloud apps. Test-first work, implementation from specs, git hygiene and scope control. Everything for building software: planning, coding, debugging, design, frameworks, cloud, agents and skills.
Type	type:guidelines Knowledge and rules the agent applies while it works: best practices, design principles, API references.
Effect	effect:none Adds no side effects of its own. It shapes how the agent thinks, writes or reviews. Guidelines and routers are always effect:none, even when the task they support edits code.
Needs	needs:none Instructions only. Works in any agent that supports skills.
Publisher	by:community Everyone else. Unverified vendor claims default to community.
Topics	architecture infrastructure
Stack	stack:amazon stack:azure stack:google-cloud stack:oci

SKILL.md

From wshobson/agents at 4bde316. Checked daily, last changed Nov 6, 2026.

Frontmatter

name: multi-cloud-architecture
description: 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