multi-cloud-architecture

Name: multi-cloud-architecture
Author: ormus-solutions

github.com/ormus-solutionsCloud & DevOps12378 installsUpdated 4w ago

About multi-cloud-architecture

A decision guide for architects selecting services and designing applications across AWS, Azure, and GCP. It compares services and outlines cloud distribution, abstraction, migration, and cost strategies.

Key features

01AWS、Azure、GCP service comparisons
02Multi-cloud architecture patterns
03Cloud-agnostic abstraction approaches
04Cost factors and optimization strategies
05Phased cloud migration guidance

Use cases

01Choosing services for a workload
02Planning disaster recovery across providers
03Designing cloud-independent applications
04Assessing and migrating existing infrastructure

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ormus-solutions/libreuiux-claude-code --skill multi-cloud-architecture

After installing, type/multi-cloud-architecturein Claude Code to confirm it loaded.

Details

From the project README

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

Comments

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Details

Author: ormus-solutions
Repository: ormus-solutions/libreuiux-claude-code
Category: Cloud & DevOps
Installs: 78
GitHub stars: 123

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