Can I use this for cost optimization?

Yes, it includes specific strategies for comparing compute pricing, leveraging reserved/spot instances, and optimizing data transfer costs across multiple providers.

What cloud providers does this skill cover?

This skill provides detailed comparisons and architectural patterns for the three primary global providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Does this skill help with cloud migration?

Absolutely. It defines a four-phase migration strategy—Assessment, Pilot, Migration, and Optimization—to ensure a structured and low-risk transition between environments.

How does this skill help avoid vendor lock-in?

It provides frameworks for cloud-agnostic abstractions using tools like Kubernetes, S3-compatible APIs, and open-source database alternatives to ensure workload portability.

Multi-Cloud Architecture

Name: Multi-Cloud Architecture
Author: Microck

byMicrock

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Cloud Infrastructure

Designs resilient multi-cloud architectures by comparing services and implementing cross-platform patterns across AWS, Azure, and GCP.

About

This skill provides a comprehensive decision framework for building cloud-agnostic systems and managing complex deployments across multiple major providers. It equips developers with detailed service mapping tables, architectural patterns like Disaster Recovery and Best-of-Breed selection, and cloud-native abstraction strategies to prevent vendor lock-in. By leveraging integrated migration phases and cost optimization tactics, it helps teams select the most efficient services from AWS, Azure, and GCP while ensuring high availability, scalability, and regional compliance.

Key Features

81 GitHub stars
Frameworks for multi-cloud disaster recovery and geographic distribution
Comparative cost analysis and optimization strategies across providers
Cross-provider service mapping for compute, storage, and database tiers
Cloud-agnostic abstraction patterns using Kubernetes and Terraform
Multi-phase migration strategies from assessment to optimization

Use Cases

Designing high-availability disaster recovery plans across different cloud providers
Architecting cloud-agnostic systems to mitigate vendor lock-in and optimize costs
Selecting 'best-of-breed' services for hybrid workloads (e.g., AI on GCP, Enterprise on Azure)

About

Key Features

81 GitHub stars
Frameworks for multi-cloud disaster recovery and geographic distribution
Comparative cost analysis and optimization strategies across providers
Cross-provider service mapping for compute, storage, and database tiers
Cloud-agnostic abstraction patterns using Kubernetes and Terraform
Multi-phase migration strategies from assessment to optimization

Use Cases

Designing high-availability disaster recovery plans across different cloud providers
Architecting cloud-agnostic systems to mitigate vendor lock-in and optimize costs
Selecting 'best-of-breed' services for hybrid workloads (e.g., AI on GCP, Enterprise on Azure)