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Introduction: Epic Outage Strikes Again
On October 20, 2025, at 12:11 AM EDT, AWS US-East-1 region experienced a massive outage lasting approximately 6.5 hours. The scale was staggering: 6.5 million user reports, 1000+ affected companies, 59 AWS services disrupted, and 64 internal services failed.
Major services like Snapchat, Roblox, Fortnite, Duolingo, Coinbase, and United Airlines were completely down. This wasn’t US-East-1’s first rodeo—it’s happened again and again.
Key Question: As an architect or technical decision-maker, how should we design disaster recovery architectures to handle massive cloud provider outages? Is Multi-Cloud really necessary? How do we balance cost and risk?
This article provides comprehensive technical analysis and practical recommendations.
Outage Impact: By the Numbers
2025-10-20 Event Statistics
| Metric | Data |
|---|---|
| User Reports | 6.5M+ |
| Affected Companies | 1000+ |
| AWS Services Down | 59 public services |
| Internal Services | 64 internal services |
| Outage Duration | ~6.5 hours |
| Traffic Handled | US-East-1 = 35-40% global |
Historical Comparison
Fastly CDN Outage (June 8, 2021):
– Traffic Loss: 75% of Fastly traffic vanished
– Service Disruption: 85% services affected
– Duration: 1 hour
Cloudflare Outage (June 24, 2021):
– Traffic Drop: 15% network-wide
– Duration: 2 hours
AWS US-East-1 Epic Outage (December 7, 2021):
– Duration: 6.5 hours (AWS’s longest)
– Traffic Estimate: 35-40% of global AWS traffic
Cloud Provider Actual Availability (2024 Data)
| Provider | Promised SLA | Actual Availability | Annual Downtime |
|---|---|---|---|
| Azure | 99.9-99.99% | 99.995% | ~26 minutes |
| AWS | 99.9-99.99% | 99.99% | ~52 minutes |
| GCP | 99.5-99.99% | 99.9-99.99% | ~52 min-8.7 hrs |
Key Observations:
– SLA promises ≠ actual performance
– Azure slightly outperforms AWS on average
– Only 25% of cloud regions had zero incidents in 2024 (29/116)
Why US-East-1 is the “Death Zone”
Technical Debt and Legacy Baggage
US-East-1 is AWS’s oldest region (launched 2006), carrying 19 years of technical debt. Worse, global services like IAM, DynamoDB Global Tables, and Route53 depend on it.
Vicious Cycle:
US-East-1 is most critical
↓
Update risk extremely high
↓
Update frequency decreases
↓
Technical debt accumulates
↓
Becomes increasingly fragile
Today’s Root Cause
DNS Failure Cascade:
- DynamoDB DNS Failure → Unable to resolve DynamoDB API endpoint
- All DynamoDB-dependent services fail → EC2, Lambda, S3 cascade
- Global IAM services affected → Cannot log into AWS Console
- Complete loss of control → Must wait for AWS to fix
DNS is the “address book” for all cloud services. US-East-1’s DNS serves global features, amplifying single points of failure into global disasters.
US-East-1’s Special Status
| Characteristic | Impact |
|---|---|
| Largest Region | 35-40% of global AWS traffic |
| Cheapest | Many companies choose it to save costs |
| Global Service Dependency | IAM, Route53, CloudFront core services |
| Most Complex | 19 years of accumulated technical debt |
| Hard to Update | Any change could trigger global disaster |
Architect’s Avoidance Strategies: Five Levels
Level 1: Multi-AZ (Multiple Availability Zones)
Cost Increase: +10-20%
Complexity: ⭐⭐
Availability: 99.9% → 99.95%
Protection Scope: Datacenter-level failures
Architecture Example:
Region: US-West-2
├── AZ-1a (Primary)
│ ├── EC2 instances
│ ├── RDS Primary
│ └── Load Balancer
├── AZ-1b (Backup)
│ ├── EC2 instances
│ └── RDS Standby
└── AZ-1c (Backup)
└── EC2 instances
Cannot Protect Against:
– Today’s US-East-1 regional failure
– Global service dependencies
Use Case: Small businesses, cost-sensitive, low risk tolerance
Level 2: Multi-Region (Same Cloud, Multiple Regions)
Cost Increase: +50-100%
Complexity: ⭐⭐⭐⭐
Availability: 99.95% → 99.99%
Protection Scope: Regional failures
Architecture Example:
Primary Region: US-West-2
- Full application stack
- RDS Multi-AZ
- S3 Cross-Region Replication
Secondary Region: EU-West-1
- Full application stack (standby)
- RDS Read Replica
- S3 bucket (replica)
Traffic Manager: Route53 + Health Checks
Can Protect Against:
– US-East-1 regional failure
– Geographic disasters
Cannot Protect Against:
– AWS global issues (IAM, Route53 failures)
– Account lockouts
Real-World Case: Netflix
– 100% AWS across 3 US regions + 1 EU region
– Uses Chaos Engineering for regular testing
– Cost increase: 80-120% infrastructure costs
– Learn more about AWS CloudFront optimization strategies
Use Case: Mid-to-large enterprises, revenue-driven, compliance requirements
Level 3: Multi-Cloud Strategy
Cost Increase: +100-200%
Complexity: ⭐⭐⭐⭐⭐
Availability: 99.99% → 99.995%
Protection Scope: Single cloud global failure
Architecture Example:
Primary: AWS US-West-2
- Main application (100% traffic)
- Aurora PostgreSQL
- CloudFront CDN
Secondary: Azure West Europe (Hot Standby)
- Application deployment (0% traffic, ready)
- Azure Database for PostgreSQL (real-time replication)
- Azure CDN
DR Failover:
- DNS Failover (Route53 → Azure Traffic Manager)
- Database Replication (AWS DMS → Azure)
- Storage Sync (S3 → Azure Blob via Rclone)
- Consider [AWS WAF for security](https://blog.rajatim.com/how-to-set-up-cloudfront-ip-whitelist-with-aws-waf/) during failover
Can Protect Against:
– AWS global failures (like today)
– AWS account lockouts
– AWS policy change risks
Challenges:
– Different tech stacks: AWS Lambda ≠ Azure Functions
– High costs: Double resources + cross-cloud transfer fees
– Team skills: Need expertise in multiple platforms
– Data consistency: Cross-cloud database sync latency
Real-World Case: Siemens
– Primary: AWS (core applications)
– Analytics: GCP BigQuery (30% faster)
– DR: Azure (disaster recovery)
– Cost savings: 25% (choosing cheapest cloud per workload)
Use Case: Large enterprises, financial/healthcare industries, zero-downtime requirements
Level 4: Hybrid Cloud (Cloud + On-Premises)
Cost Increase: +150-300%
Complexity: ⭐⭐⭐⭐⭐⭐
Availability: 99.995% → 99.999%
Protection Scope: All clouds fail simultaneously
Architecture Example:
Cloud Layer:
- AWS (Primary) - 60% traffic
└── US-West-2 + EU-West-1
- Azure (Secondary) - 30% traffic
└── West Europe + East Asia
- GCP (Tertiary) - 10% traffic
└── asia-east1
On-Premises Layer:
- Core Data Center (Singapore)
├── VMware vSphere cluster
├── On-prem PostgreSQL cluster
└── Private S3-compatible storage (MinIO)
- DR Data Center (Tokyo)
└── Real-time replication
Orchestration:
- Kubernetes multi-cluster (Rancher)
- Service Mesh (Istio)
- Database Replication (Debezium CDC)
- Global Load Balancer (F5 / Cloudflare)
Can Protect Against:
– All clouds fail simultaneously
– Political risks (data localization requirements)
– Cost optimization (non-critical services on-prem saves 40-60%)
– Regulatory compliance (GDPR, healthcare data must stay on-prem)
Challenges:
– Extremely high complexity: Requires 10+ dedicated SRE team
– On-prem operational costs: Hardware depreciation, power, cooling, staff
– Network latency: Cloud ↔ on-prem typically 50-200ms
– Data consistency: Requires CDC + conflict resolution
Real-World Case: Banking (HSBC, JP Morgan)
– Core trading systems: On-prem (regulatory + ultra-low latency)
– Customer applications: AWS/Azure (elastic scaling)
– Big data analytics: GCP (cost-optimized)
– Cost: Infrastructure costs 2-3x pure cloud
Use Case: Financial institutions, government agencies, large manufacturers
Level 5: Edge Computing + Geo-Distribution
Cost Increase: +200-500%
Complexity: ⭐⭐⭐⭐⭐⭐⭐
Availability: 99.999% → 99.9999%
Protection Scope: Nuclear disaster level
Architecture Example:
Global Edge Layer:
- 300+ edge nodes globally distributed
- Static content cached at edge
- Dynamic API proxied to nearest region
Multi-Cloud Core:
- AWS: 5 regions
- Azure: 3 regions
- GCP: 2 regions
- Alibaba Cloud: 2 regions (China)
Hybrid On-Premises:
- Primary DC (Singapore)
- Secondary DC (Tokyo)
- Tertiary DC (London)
Data Layer:
- CockroachDB (geo-distributed SQL)
- Cassandra (NoSQL for analytics)
- Object Storage: Multi-cloud
Orchestration:
- Kubernetes Federation
- Istio Service Mesh
- Consul (service discovery)
- Terraform (IaC for all platforms)
Real-World Case: Cloudflare
– 300+ data centers globally
– Any region failure = automatic failover, user-transparent
– Cost: Single outage cost < revenue loss
Use Case: Global SaaS, financial trading platforms, online gaming, CDN providers
Cost vs Risk: The Harsh Truth
Cost Comparison Table
| Strategy | Base Cost | Extra Cost | Complexity | Availability | Downtime Risk |
|---|---|---|---|---|---|
| Single-AZ | $10K/mo | – | ⭐ | 99.5% | Very High |
| Multi-AZ | $10K/mo | +20% | ⭐⭐ | 99.9% | High |
| Multi-Region | $10K/mo | +80% | ⭐⭐⭐⭐ | 99.99% | Medium |
| Multi-Cloud | $10K/mo | +150% | ⭐⭐⭐⭐⭐ | 99.995% | Low |
| Hybrid Cloud | $10K/mo | +250% | ⭐⭐⭐⭐⭐⭐ | 99.999% | Very Low |
Hourly Downtime Cost (Industry Average)
| Industry | Cost per Hour | 6-Hour Loss |
|---|---|---|
| Financial Trading | $5.4M | $32.4M |
| E-commerce | $1M | $6M |
| SaaS | $300K | $1.8M |
| General Corporate | $50K | $300K |
ROI Calculation Example
Assumptions:
– You’re an e-commerce platform, monthly revenue $5M
– Annual downtime risk: Multi-AZ = 8 hours, Multi-Region = 1 hour
– Downtime cost: $1M/hour
Multi-AZ ($12K/month):
– Annual downtime loss: 8 hours × $1M = $8M
– Extra infrastructure cost: $24K/year
– Net loss: $8M
Multi-Region ($18K/month):
– Annual downtime loss: 1 hour × $1M = $1M
– Extra infrastructure cost: $96K/year
– Net loss: $1M
– ROI: Save $7M
Conclusion: For e-commerce, Multi-Region is a wise investment.
Is Azure / GCP Really Better?
Objective Data Comparison (2024-2025)
| Metric | AWS | Azure | GCP |
|---|---|---|---|
| Market Share | 32% | 23% | 11% |
| Global Regions | 33 | 60+ | 40+ |
| Actual Availability | 99.99% | 99.995% | 99.99% |
| Major 2024 Outages | 3 | 2 | 1 |
Azure Pros & Cons
Pros:
– Enterprise integration: Seamless with Active Directory, Office 365
– Windows Server licensing costs 40% lower
– Most mature hybrid cloud (Azure Arc)
– Slightly better availability
Cons:
– Ecosystem less mature than AWS
– Fewer third-party tool integrations
– Steeper learning curve
GCP Pros & Cons
Pros:
– Kubernetes native (Google invented it)
– BigQuery analytics 30-50% faster
– Fastest global network backbone
– Machine learning leadership (TensorFlow, Vertex AI)
Cons:
– Smallest market share, weakest ecosystem
– Enterprise support inferior to AWS/Azure
– Complex pricing
Selection Strategy
Startups (< 10 people):
– Choose AWS: Most complete ecosystem
Windows-Heavy Users:
– Choose Azure: Lower licensing costs, best integration
AI/ML Core:
– Choose GCP: BigQuery + Vertex AI unbeatable
Large Enterprises (Multi-Cloud):
– Primary: AWS (highest maturity)
– DR: Azure (most regions, high availability)
– Analytics: GCP BigQuery (best performance + cost)
Disaster Recovery Architecture Upgrade Recommendations: Phased Implementation Roadmap
Current Risk Assessment
Based on the 2025-10-20 AWS outage incident, the technical team has completed an internal architecture risk assessment:
Architecture Risk Matrix:
| Assessment Item | Current Status | Risk Level | Potential Annual Loss |
|---|---|---|---|
| Single Cloud Dependency | AWS 100% | 🔴 High | $500K-2M |
| Region Concentration | US-East-1 | 🔴 Critical | $1M-5M |
| Data Backup Strategy | Single Region | 🟡 Medium | $200K-500K |
| Disaster Recovery Plan | No RTO/RPO | 🔴 High | $800K-3M |
| Monitoring & Alerting | Reactive | 🟡 Medium | $100K-300K |
Key Findings:
1. Core services over-rely on US-East-1 (35% of global AWS traffic, most vulnerable region)
2. No cross-region automatic failover mechanism
3. RTO (Recovery Time Objective) undefined, estimated > 6 hours
4. Database lacks Multi-AZ configuration, single point of failure risk
Recommended Solutions: Three-Phase Upgrade Path
Phase One: Emergency Risk Mitigation (Complete within 30 days)
Objective: Reduce single point of failure risk by 60%
Required Actions:
- Database High Availability Transformation
- Enable RDS Multi-AZ (one-click activation, downtime < 2 minutes)
- Estimated cost increase: +20% ($2K/month → $2.4K/month)
-
Availability improvement: 99.5% → 99.9%
-
Critical Data Cross-Region Backup
- Enable S3 Cross-Region Replication (US-West-2 → EU-West-1)
- RDS automated snapshot retention: 1 day → 7 days
-
Cost increase: +$500/month (storage fees)
-
Basic Monitoring & Alerting
- Deploy AWS Personal Health Dashboard
- Configure CloudWatch Alarms (RDS, EC2, ALB)
- Integrate PagerDuty/Slack real-time notifications
-
Cost: $200/month
-
Establish DR Runbook
- Document manual failover procedures
- Define RTO: 4 hours, RPO: 1 hour
- Quarterly DR drills
Return on Investment:
– Total cost: $3.1K/month (+15%)
– Risk reduction: Potential annual loss from $2M → $800K
– ROI: 3-month payback period
Decision Point: This phase is basic protection, recommended for immediate execution without board approval.
Phase Two: Multi-Region Architecture (3-6 months)
Objective: Achieve region-level disaster automatic recovery
Technical Approach:
- Phase 2A: Passive DR (Warm Standby)
- Secondary region: US-West-2 (Oregon)
- RDS Read Replica (automatic sync, latency < 5 seconds)
- EC2 Auto Scaling pre-configured (0 instances, fast scale-up)
- Route53 Health Check + automatic DNS Failover
-
Estimated RTO: 15 minutes, RPO: 5 seconds
-
Phase 2B: Active-Active
- Both regions serve traffic simultaneously (US-West-2 70%, EU-West-1 30%)
- DynamoDB Global Tables (multi-master replication)
- Aurora Global Database (cross-region writes, latency < 1 second)
- Estimated RTO: 0 minutes (automatic failover), RPO: < 1 second
Cost Analysis:
| Item | Phase 2A (Warm) | Phase 2B (Active-Active) |
|---|---|---|
| Compute Resources | +40% | +100% |
| Database | +30% | +80% |
| Network Transfer | +10% | +20% |
| Total Cost Increase | +50% | +100% |
| Monthly Fee | $6K → $9K | $6K → $12K |
ROI Analysis (E-commerce Platform Example):
Assumptions:
– Monthly revenue: $5M
– Outage cost: $1M/hour
– Annual outage risk: 8 hours → 1 hour
Phase 2A Benefits:
Annual outage loss savings: 7 hours × $1M = $7M
Additional infrastructure cost: $36K/year
Net benefit: $6.96M/year
ROI: 19,333%
Payback period: 1.9 days
Decision Point: Recommend prioritizing Phase 2A (Warm Standby) for optimal cost-benefit ratio. Phase 2B depends on business continuity requirements (recommended for financial and trading systems).
Implementation Recommendations:
– Q1: Complete architecture design & POC
– Q2: Production environment deployment & testing
– Q3: First official disaster drill
Phase Three: Multi-Cloud Strategy Evaluation (6-12 months)
Objective: Eliminate single cloud global failure risk
Evaluation Framework:
This phase is not for immediate execution but for feasibility assessment. Recommend establishing a cross-functional working group (Architecture, DevOps, Finance, Legal) to conduct the following analysis:
1. Business Requirements Assessment
| Assessment Item | Question | Decision Impact |
|---|---|---|
| Regulatory Compliance | Data localization requirements? | If yes → Must go Multi-Cloud |
| Customer SLA | Committed availability? | 99.95%+ → Consider Multi-Cloud |
| Outage Cost | Loss per hour? | > $500K → Strongly recommended |
| Competitive Advantage | Competitor DR capabilities? | Falling behind → Strategic necessity |
2. Technical Feasibility Analysis
Assessment Items:
- [ ] Application architecture decoupling level (Microservices vs Monolith)
- [ ] Cross-cloud database synchronization solution (AWS DMS, Debezium CDC)
- [ ] Storage layer cross-cloud strategy (S3 ↔ Azure Blob sync)
- [ ] Network connectivity (VPN, Direct Connect costs)
- [ ] Team skill gaps (Azure/GCP training needs)
3. Cost-Benefit Model
Option A: AWS Primary + Azure DR (Recommended)
Architecture:
- AWS US-West-2 (primary, 100% traffic)
- Azure West Europe (Hot Standby, 0% traffic)
Cost Structure:
- AWS existing cost: $10K/month
- Azure DR cost: $5K/month (compute standby + data sync only)
- Total cost: $15K/month (+50%)
Expected Benefits:
- Protect against AWS global failures (like 2025-10-20 incident)
- RTO: 10 minutes (DNS switch + application startup)
- RPO: 5 minutes (data sync latency)
Option B: AWS + Azure + GCP (Tri-Cloud)
Architecture:
- AWS (primary, 60% traffic)
- Azure (secondary, 30% traffic)
- GCP (tertiary, 10% traffic + big data analytics)
Cost Structure:
- Total cost: $25K/month (+150%)
Applicable Scenarios:
- Financial trading platforms (zero downtime requirement)
- Global SaaS (multi-region compliance)
- Data-intensive applications (leveraging GCP BigQuery)
4. Implementation Timeline & Milestones
Month 1-3: Requirements Definition & POC
- Select pilot service (non-critical)
- Azure environment setup
- Cross-cloud data sync validation
Month 4-6: Small-Scale Production Deployment
- Migrate 1-2 microservices to Azure
- Disaster recovery drill
- Monitoring & alerting integration
Month 7-9: Gradual Scale-Up
- 20% of services with Azure failover capability
- CI/CD automation optimization
Month 10-12: Evaluation & Decision
- Actual cost data analysis
- Team capability maturity assessment
- Decide whether to fully implement
5. Risks & Challenges
| Risk Type | Specific Risk | Mitigation Measures |
|---|---|---|
| Technical Complexity | Cross-cloud data consistency difficult | Use mature solutions (AWS DMS, Debezium) |
| Cost Overrun | Actual costs 50% over estimates | Strict cost monitoring (CloudHealth, CloudCheckr) |
| Team Skills | Lack Azure/GCP experience | Certification training program (3 months) |
| Vendor Lock-in | Over-customization difficult to migrate | Prioritize open-source, standardized tech (Kubernetes, Terraform) |
Decision Recommendations:
Execute Immediately (Recommended):
– Phase One: Emergency risk mitigation (30 days, +15% cost)
– Phase Two 2A: Warm Standby (6 months, +50% cost)
Decide After Evaluation:
– Phase Two 2B: Active-Active (based on SLA requirements)
– Phase Three: Multi-Cloud (based on regulatory, competitive needs)
Defer Execution (Unless Special Requirements):
– Hybrid Cloud (only for financial institutions, government agencies)
– Edge Computing (only for global SaaS, CDN providers)
Budget & Resource Requirements
Year 1 Investment Plan:
| Phase | Timeline | Capital Expenditure | Operating Cost Increase | Staffing Needs |
|---|---|---|---|---|
| Phase One | Q1 | $5K | +$3.1K/month | Existing team |
| Phase Two A | Q2-Q3 | $20K | +$3K/month | +1 DevOps |
| Phase Three Eval | Q4 | $30K | – | Cross-functional team |
| Year 1 Total | – | $55K | +$6.1K/month | +1 person |
Year 2-3 (If Executing Multi-Cloud):
– Capital expenditure: $100K-200K (Azure/GCP environment setup)
– Operating cost: +$5-10K/month
– Staffing needs: +2-3 people (Multi-Cloud SRE)
Success Metrics (KPIs)
Technical Metrics:
– Availability: 99.5% → 99.9% (Phase One) → 99.95% (Phase Two)
– RTO: Undefined → 4 hours → 15 minutes
– RPO: Undefined → 1 hour → 5 seconds
– Disaster drill success rate: 0% → 80%+
Business Metrics:
– Annual downtime: Estimated 8 hours → 1 hour
– Outage-related losses: $8M → $1M
– Customer satisfaction (NPS): +10 points
– Corporate brand risk: Reduced 70%
Competitor Analysis
Industry DR Maturity:
| Company | Architecture Strategy | Availability | Insight |
|---|---|---|---|
| Netflix | Multi-Region (AWS 100%) | 99.99% | Single cloud but multi-region achieves 4 nines |
| Stripe | Multi-Cloud (AWS + GCP) | 99.995% | Finance requires Multi-Cloud |
| Spotify | Multi-Cloud (GCP + AWS) | 99.9% | Leveraging GCP big data advantages |
| Us | Single-Region (AWS) | 99.5% | Behind industry |
Key Decision Questions
Decision Questions for C-Level:
- Risk Tolerance: What annual downtime is acceptable?
- Option A: 8 hours/year (status quo, high risk)
- Option B: 1 hour/year (Multi-Region, recommended)
-
Option C: < 5 minutes/year (Multi-Cloud, high cost)
-
Investment Priority: DR architecture vs new feature development?
-
Recommendation: Allocate 20% of Year 1 technical budget to DR ($55K)
-
Timeline Requirements: When must this be completed?
-
Recommendation: Phase One immediate (30 days), Phase Two Q2-Q3
-
Team Expansion: Approve hiring 1 additional DevOps?
- Recommendation: Approve (salary $120K, but prevents $1M+ outage losses)
Conclusion:
Based on the AWS US-East-1 outage incident, current architecture risk is assessed as “High.” Recommend immediate execution of Phase One (risk mitigation) and completion of Phase Two (Multi-Region) within 6 months. Multi-Cloud strategy depends on business requirements and regulatory needs; recommend feasibility assessment first.
Expected Benefits:
– Investment: Year 1 $55K + $73K operating costs
– Returns: Avoid $7M annual outage losses
– ROI: 5,395%
– Payback period: 2.6 days
Architect’s Core Mindset
Today’s Lessons:
- Never Trust SLA: 99.99% ≠ won’t fail
- Single Cloud = Single Point of Failure: Even AWS
- US-East-1 is Poison: Cheap but costly
- Cost is Insurance: Multi-cloud isn’t waste, it’s risk hedging
- Drills Determine Survival: Untested DR plan = no plan
Design Principles:
Design for Failure
↓
Assume everything will fail
↓
Build redundancy at every layer
↓
Automate recovery
↓
Test, test, test
Conclusion
The October 20, 2025 AWS outage proves once again: cloud providers aren’t gods, and US-East-1 certainly isn’t. 6.5 million users’ lesson: disaster recovery isn’t optional, it’s mandatory.
Recommendations by Company Size:
| Company Type | Strategy | Cost Increase | Implementation |
|---|---|---|---|
| Individual / Small | Multi-AZ | +20% | 1 week |
| Startup | Multi-Region (2) | +60% | 1-2 months |
| Growth Stage | Multi-Region + Multi-Cloud DR | +100% | 3-6 months |
| Public Company | Multi-Cloud + Hybrid | +200% | 12-18 months |
| Financial/Healthcare | Full Geo-Distribution | +300% | 24 months |
Do Tomorrow:
- Check if architecture is on US-East-1 → If yes, plan migration immediately
- Enable RDS Multi-AZ
- Enable S3 Versioning + Cross-Region Replication
Is your architecture ready?
References
Official Resources:
– AWS Well-Architected Framework
– Azure Architecture Center
– GCP Solutions Architecture
Tools:
– Terraform: Multi-cloud IaC
– Kubernetes: Cross-cloud container orchestration
– Datadog / New Relic: Unified monitoring