multi-cloud-architecture
Helps your agent plan how to use multiple cloud providers like AWS, Azure, and GCP. It offers frameworks and guidance for building applications that work across different clouds.
What it does
This tool helps plan how to use services from different cloud providers (like AWS, Azure, and Google Cloud) to build applications. It offers patterns and comparisons to aid decision-making.
When to use it
- ✓When planning how to use multiple clouds for a project.
- ✓When deciding which cloud service is best for a specific task.
- ✓When thinking about moving applications between clouds.
Example of what to ask your AI
“I want to build an application that uses the best AI service from Google Cloud and the best compute service from AWS. What services do you recommend and how can I connect them?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool compares compute, storage, and database services across AWS, Azure, and Google Cloud. It also suggests architecture patterns and strategies for using multiple clouds effectively.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill multi-cloud-architectureOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/multi-cloud-architecture/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Bundled upstream support
Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes.
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.
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
| 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
…
FAQ
What is 'multi-cloud'?
It means using services from more than one cloud provider (like AWS, Azure, Google Cloud) at the same time, instead of relying on just one.
Why use multiple clouds?
To take advantage of the best services from each provider, avoid vendor lock-in, improve resilience, or meet specific location requirements.
What are 'multi-cloud architecture patterns'?
These are proven blueprints for organizing applications and services across different clouds, such as using a primary cloud with a backup in another, or picking the best service from each for different parts of the application.
Details
Original description by the author
Decision framework and patterns for architecting applications across AWS, Azure, and GCP.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3646
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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