Cloud-Agnostic Principles. Platform-Specific Execution.
A comprehensive consulting framework for organizations seeking to modernize infrastructure, adopt AI, and build engineering excellence — without vendor lock-in.
| You Are | Your Challenge | This Playbook Helps You |
|---|---|---|
| CTO/Engineering Leader | "We're stuck on [Azure/AWS/GCP] but need to think strategically about our tech stack" | Make platform decisions based on business outcomes, not sales pitches |
| Enterprise Architect | "I need architecture patterns that work across clouds" | Access universal principles with platform-specific implementation guides |
| Technical Consultant | "My clients need help choosing between Azure, AWS, and GCP" | Deliver objective assessments using proven decision frameworks |
| SME/Scale-up | "We're growing fast and need to avoid costly mistakes" | Build on battle-tested patterns that scale without over-engineering |
Most consulting focuses on "How do we implement this in [specific cloud]?"
We focus on "What engineering principles solve your business problem, and which platform best executes them?"
┌─────────────────────────────────────────────┐
│ Layer 1: EXPERIENCE │ ← User-Facing (Power Platform, Apps, Copilots)
│ What users see and interact with │
├─────────────────────────────────────────────┤
│ Layer 2: INTELLIGENCE │ ← Data & AI (Lakehouse, RAG, Knowledge Graphs)
│ How data becomes actionable insights │
├─────────────────────────────────────────────┤
│ Layer 3: INFRASTRUCTURE │ ← Foundation (Compute, Storage, Security, Network)
│ The invisible foundation that enables 1 & 2│
└─────────────────────────────────────────────┘
The principles stay constant. Only the implementation changes.
→ Start Here: Decision Guide: Which Cloud Platform?
- Understand the trade-offs between Azure, AWS, and GCP
- See real-world cost comparisons
- Learn when to choose single-cloud vs. multi-cloud
→ Start Here: Architecture Principles
- Platform-agnostic patterns (microservices, event-driven, data mesh)
- Security-by-design frameworks (zero-trust, identity-first)
- Cloud-native design principles (12-factor, CNCF patterns)
→ Start Here: Implementation Guides
- Azure: See our comprehensive Azure CoE repo
- AWS: Coming soon
- GCP: Coming soon
- Multi-Cloud/Hybrid: Kubernetes & Terraform patterns
→ Start Here: Assessment Methodology
- Cloud readiness assessment templates
- AI maturity models
- Well-Architected Framework (platform-agnostic)
Universal engineering principles that transcend platforms:
- Architecture Patterns: Microservices, event-driven, data mesh, CQRS, domain-driven design
- AI Fundamentals: RAG, agents, LLM orchestration (vendor-neutral)
- Data Engineering: Medallion architecture, lakehouse, data lineage
- Security by Design: Zero-trust, identity-first, encryption at rest/in-transit
- Cloud-Native Design: 12-factor apps, CNCF patterns, container-first
- Engineering Culture: Team topologies, DevOps practices, continuous improvement
How to choose technologies objectively:
- Platform Selection: Azure vs AWS vs GCP comparison
- Technology Radar: Assess/Trial/Adopt framework (Thoughtworks-inspired)
- Build vs Buy: Decision matrices for custom vs. off-the-shelf
- Database Selection: SQL, NoSQL, vector, graph, time-series
- AI Model Selection: OpenAI, Anthropic, open-source, fine-tuning
Platform-specific HOW-TOs:
- Azure: Comprehensive reference implementation
- AWS: Placeholder (contact for AWS-specific consulting)
- GCP: Placeholder (contact for GCP-specific consulting)
- Hybrid/Multi-Cloud: Kubernetes, Terraform, cloud-agnostic IaC
- Platform Comparisons: Side-by-side implementation guides
Evaluate current state and plan future state:
- Technical Debt Audit: Identify and prioritize technical debt
- Cloud Readiness: Pre-migration assessment frameworks
- AI Maturity Model: Assess AI/ML capabilities and readiness
- Well-Architected Review: 5 pillars across Azure/AWS/GCP
🎒 Consulting Toolkit NEW
Ready-to-use materials for client engagements:
- AI Maturity Assessment: 5-dimension assessment with scoring guide
- Sales Materials: Competitive positioning, ROI calculators
- Proposal Templates: Level 1/2/3 engagement templates
- Case Studies: Industry-specific success stories with ROI data
Vertical-specific patterns and compliance:
- Healthcare: HIPAA, FHIR, patient data protection
- Financial Services: PCI-DSS, fraud detection, regulatory compliance
- Manufacturing: IoT, predictive maintenance, Industry 4.0
- Retail: E-commerce, personalization, inventory optimization
Real-world transformations:
- Multi-cloud data mesh implementations
- Platform migrations (Azure ↔ AWS)
- AI adoption journeys
- Cost optimization success stories
Curated resources (we don't reinvent the wheel):
- External Frameworks: MIT CISR, OWASP AIMA, ThoughtWorks Radar
- Certification paths (Azure, AWS, GCP, Kubernetes)
- Recommended reading (books, whitepapers, videos)
- CNCF Landscape guide
🛠️ Tools
Practical templates and calculators:
- Cost calculators (multi-cloud TCO comparison)
- Architecture diagram templates (draw.io, Mermaid, C4)
- Assessment scorecards (Excel, Google Sheets)
❌ "You need Azure because you use Office 365" ❌ "AWS is the market leader, so go with AWS" ❌ "Multi-cloud is too complex, pick one" ❌ "Let's implement AI because it's trendy"
✅ "Let's assess your business needs, existing investments, and team skills — then recommend the optimal platform" ✅ "Here's how Azure, AWS, and GCP solve this problem differently, with cost and complexity trade-offs" ✅ "Multi-cloud adds operational overhead — let's determine if your use case justifies it" ✅ "AI should solve specific business problems with measurable ROI — let's start with your data foundation"
We care about business results (revenue, cost savings, compliance), not lines of code or number of microservices.
The best architecture is the simplest one that meets your requirements. We avoid over-engineering.
You can't do AI without good data. We build the foundation (Layer 3 → Layer 2) before the flashy interfaces (Layer 1).
We're not anti-cloud-vendor. We're pro-informed-decision. Sometimes single-cloud is the right choice.
Monolith-first is often smarter than microservices-first. We iterate from where you are, not where Medium blog posts say you should be.
Kubernetes, Terraform, PostgreSQL, Kafka — we prefer open standards that work across platforms.
- Assess: Use assessment methodology to understand current state
- Design: Apply principles to create future-state architecture
- Decide: Use decision frameworks to choose technologies
- Implement: Follow implementation guides for specific platforms
- Measure: Track outcomes, iterate, improve
- Learn: Browse principles to understand best practices
- Compare: Use decision frameworks to evaluate options
- Implement: Follow implementation guides step-by-step
- Upskill: Use learning resources for certifications
- Understand: Read decision guide
- Assess: Review case studies for ROI examples
- Decide: Use business-outcome criteria, not technical buzzwords
- Partner: Engage consultants who think principles-first, platform-second
- 📘 Azure vs AWS vs GCP: Complete Comparison
- 📘 When to Choose Multi-Cloud (and When Not To)
- 📘 Technology Radar: What to Adopt in 2025
- 💻 Azure Reference Implementation (Full CoE Repo)
- 💻 Multi-Cloud Terraform Patterns
- 💻 Platform-Agnostic RAG Architecture
- 📊 Cloud Readiness Assessment Template
- 📊 AI Maturity Model Scorecard
- 📊 Well-Architected Review (Platform-Agnostic)
engineering-excellence-playbook/
├── principles/ # Universal patterns (architecture, AI, data, security)
├── decision-frameworks/ # How to choose (platform selection, tech radar)
├── implementation-guides/ # Platform-specific HOW-TOs (Azure, AWS, GCP)
├── assessment-methodology/ # Evaluation frameworks (cloud readiness, WAF)
├── consulting-toolkit/ # Client engagement materials (NEW)
│ ├── assessment-tools/ # AI maturity, cloud readiness assessments
│ ├── sales-materials/ # Competitive positioning, ROI calculators
│ ├── proposal-templates/ # Level 1/2/3 engagement templates
│ └── case-studies/ # Industry-specific success stories
├── industry-solutions/ # Vertical-specific patterns
├── case-studies/ # Real-world examples
├── learning-resources/ # External frameworks, certifications
└── tools/ # Templates & calculators
This playbook is the hub that connects specialized repositories:
| Repository | Purpose | Link |
|---|---|---|
| azure-enterprise-solutions-architecture | Azure CoE reference implementation | View |
| copilot-center-of-excellence | Microsoft Copilot implementation guides | View |
| three-layer-ai-framework | Core 3-layer architecture with code examples | View |
| ai-tools-platforms-solutions-guide | ThoughtWorks Radar-based tool recommendations | View |
| maturity-assessor | AI maturity assessment prototype (private) | Private |
| semantic-kernel-production-patterns | Production patterns for Semantic Kernel | View |
| enterprise-agent-toolkit | Complete consulting sales toolkit | View |
External Frameworks Referenced:
- OWASP AI Maturity Assessment - AI security & responsible AI
- MIT CISR AI Maturity Model - Enterprise AI value creation
- ThoughtWorks Technology Radar - Technology recommendations
- Repository structure
- Core principles documentation
- Azure reference implementation (complete)
- Platform comparison framework
- AWS implementation guides
- GCP implementation guides
- Multi-cloud case studies
- Video tutorials and workshops
- Open-source contributions
- Industry working groups
- Certification program
- Annual Engineering Excellence Conference
We welcome contributions from practitioners, consultants, and platform engineers!
See CONTRIBUTING.md for guidelines on:
- Adding new patterns
- Submitting case studies
- Improving documentation
- Reporting issues
| Platform | Contact |
|---|---|
| Azure | See our Azure CoE Repo |
| AWS | Contact us for AWS-specific engagements |
| GCP | Contact us for GCP-specific engagements |
| Multi-Cloud | Contact us for hybrid/multi-cloud strategy |
- 📧 Open an issue in this repository
- 💬 Start a discussion in GitHub Discussions
- 🐦 Follow us on LinkedIn for updates
This project is licensed under the MIT License - see LICENSE for details.
This playbook synthesizes wisdom from:
- Microsoft: Cloud Adoption Framework, Well-Architected Framework
- AWS: Well-Architected Framework, CAF
- Google Cloud: Architecture Framework
- Thoughtworks: Technology Radar methodology
- CNCF: Cloud-native patterns and landscape
- Martin Fowler: Software architecture patterns
- Gregor Hohpe: Enterprise integration patterns
We stand on the shoulders of giants and aim to make their collective wisdom accessible to SMEs and mid-market companies.
Built with ❤️ for organizations who want engineering excellence without vendor lock-in.