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Cloud Well-Architected Framework Principles Cheat Sheet

Cloud Well-Architected Framework Principles Cheat Sheet

Back to Cloud Computing
Updated 2026-05-25
Next Topic: Cloud Workload Protection Platform (CWPP) Cheat Sheet

The Cloud Well-Architected Framework is a systematic approach to evaluating and implementing cloud architectures across AWS, Azure, and Google Cloud Platform. AWS structures its guidance through six core pillars—operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability—while Azure uses five pillars (omitting a standalone sustainability pillar), and GCP adds cross-pillar perspectives for domains like AI/ML and financial services. A critical insight often overlooked: trade-offs between pillars are intentional and necessary; optimizing for one pillar (like cost) may temporarily reduce another (like performance), and the framework helps make these decisions transparent rather than avoiding them. As AI workloads have become mainstream, each provider has released dedicated AI guidance—most notably the AWS Well-Architected Generative AI Lens (2025)—extending the framework to address non-deterministic, model-driven systems.

What This Cheat Sheet Covers

This topic spans 25 focused tables and 200 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Framework FoundationsTable 2: Operational Excellence PillarTable 3: Security PillarTable 4: Reliability PillarTable 5: Performance Efficiency PillarTable 6: Cost Optimization PillarTable 7: Sustainability PillarTable 8: Architectural Decision-MakingTable 9: Well-Architected Review ProcessTable 10: Cloud Design PatternsTable 11: Deployment StrategiesTable 12: Observability and MonitoringTable 13: Security Best PracticesTable 14: Cost Management StrategiesTable 15: Reliability PatternsTable 16: Migration Strategies (7Rs)Table 17: API Design Best PracticesTable 18: Data ManagementTable 19: Identity and Access ManagementTable 20: Performance Optimization TechniquesTable 21: Common Anti-Patterns to AvoidTable 22: Service Mesh CapabilitiesTable 23: Cloud Governance FrameworkTable 24: AI and Generative AI Workload ArchitectureTable 25: Twelve-Factor App Principles

Table 1: Framework Foundations

Understanding the structural concepts of the Well-Architected Framework is a prerequisite for applying it well. The distinctions between providers—AWS's six pillars, Azure's five, GCP's cross-pillar perspectives—matter because they shape how each provider organizes its prescriptive guidance and review tooling.

ConceptExampleDescription
Shared Responsibility Model
Provider secures infrastructure (hardware, network, facilities); customer secures workloads (data, applications, identity, access)
• Cloud security is divided between provider and customer
• understanding this boundary prevents gaps in protection and misplaced expectations
Well-Architected Pillars
AWS: 6 pillars (operational excellence, security, reliability, performance efficiency, cost optimization, sustainability); Azure: 5 pillars (same minus sustainability); GCP: 6 pillars + cross-pillar perspectives
• Framework organizes architectural best practices into pillar-specific areas that must be balanced — provider counts differ
• always check the framework you're targeting
Well-Architected Review
Structured assessment using questions aligned to pillars; identifies high-risk issues (HRIs) and medium-risk issues (MRIs)
• Formal process to evaluate workload against framework
• produces prioritized improvement plans with risk ratings
Workload
A set of components (applications, data, infrastructure) that together deliver business value
• Unit of analysis in the framework
• reviews assess individual workloads rather than entire organizations
Architectural Trade-offs
Increasing cache size improves performance but raises cost; adding redundancy improves reliability but increases complexity
• Every architectural decision involves intentional trade-offs
• the framework helps make them explicit and data-driven

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