GCP & Azure Fundamentals for Multi-Cloud
Multi-Cloud Comparison & Decision Framework
Solutions Architects must navigate multi-cloud discussions with confidence. This lesson provides comparison frameworks for interview scenarios.
Service Equivalency Matrix
Compute Services
| Category | AWS | GCP | Azure |
|---|---|---|---|
| VMs | EC2 | Compute Engine | Virtual Machines |
| Serverless Compute | Lambda | Cloud Functions | Azure Functions |
| Container Serverless | Fargate | Cloud Run | Container Apps |
| Kubernetes | EKS | GKE | AKS |
| Batch Processing | AWS Batch | Batch | Azure Batch |
Storage Services
| Category | AWS | GCP | Azure |
|---|---|---|---|
| Object Storage | S3 | Cloud Storage | Blob Storage |
| Block Storage | EBS | Persistent Disk | Managed Disks |
| File Storage | EFS | Filestore | Azure Files |
| Archive | S3 Glacier | Archive Storage | Archive Storage |
| Cold Tier | S3-IA | Nearline/Coldline | Cool/Cold |
Database Services
| Category | AWS | GCP | Azure |
|---|---|---|---|
| Relational (Managed) | RDS | Cloud SQL | Azure SQL |
| Relational (Cloud-Native) | Aurora | AlloyDB/Spanner | Azure SQL Hyperscale |
| NoSQL Key-Value | DynamoDB | Firestore/Bigtable | Cosmos DB |
| Data Warehouse | Redshift | BigQuery | Synapse Analytics |
| Cache | ElastiCache | Memorystore | Azure Cache |
| Graph | Neptune | - | Cosmos DB (Gremlin) |
Networking Services
| Category | AWS | GCP | Azure |
|---|---|---|---|
| Virtual Network | VPC | VPC | VNet |
| Load Balancer (L4) | NLB | Network LB | Azure LB |
| Load Balancer (L7) | ALB | HTTP(S) LB | App Gateway |
| CDN | CloudFront | Cloud CDN | Azure CDN/Front Door |
| DNS | Route 53 | Cloud DNS | Azure DNS |
| VPN | Site-to-Site VPN | Cloud VPN | VPN Gateway |
| Direct Connect | Direct Connect | Dedicated Interconnect | ExpressRoute |
Interview Question: Multi-Cloud Selection
Q: "A financial services company is evaluating AWS, GCP, and Azure. How would you approach the decision?"
A: Use a structured evaluation framework:
1. Existing Technology Stack
| Factor | AWS Wins | GCP Wins | Azure Wins |
|---|---|---|---|
| Microsoft Ecosystem | - | - | Office 365, AD, Teams |
| Google Workspace | - | Gmail, Drive integration | - |
| VMware Workloads | VMware Cloud on AWS | Google VMware Engine | Azure VMware Solution |
| Oracle Workloads | RDS Oracle | Bare Metal | Azure Oracle partnership |
2. Workload Requirements
Data Analytics Heavy:
- GCP: BigQuery (serverless, ML integration)
- AWS: Redshift (mature, broad ecosystem)
- Azure: Synapse (Power BI integration)
Enterprise Applications:
- Azure: SAP, Microsoft apps, AD
- AWS: Broadest SAP support
- GCP: Growing SAP support
Machine Learning:
- GCP: Vertex AI, TPUs, BigQuery ML, Gemini API
- AWS: SageMaker AI (rebranded 2024), Bedrock for foundation models
- Azure: Azure Machine Learning, Azure AI Foundry (formerly AI Studio), Azure OpenAI
3. Global Network & Latency
| Provider | Network Strength |
|---|---|
| GCP | Premium tier (Google backbone), global LB |
| AWS | Largest footprint (36+ regions worldwide) |
| Azure | 60+ regions, strong in government |
4. Cost Considerations
Cost Leaders by Category:
- Compute (sustained): GCP (automatic sustained discounts)
- Egress: GCP (generally lower than AWS)
- Storage: Azure (competitive in enterprise tiers)
- Spot/Preemptible: Similar across all (80-90% discount)
Multi-Cloud Architecture Patterns
Pattern 1: Best-of-Breed
Use each cloud for its strengths:
AWS: Core infrastructure, web applications
GCP: BigQuery for analytics, ML training
Azure: Office 365 integration, identity
Pros: Optimal for each workload Cons: Operational complexity, data movement costs
Pattern 2: Active-Passive DR
Primary on one cloud, DR on another:
Primary: AWS (us-east-1, us-west-2)
DR: Azure (East US 2)
Pros: Vendor independence, true DR Cons: Skill duplication, sync complexity
Pattern 3: Geo-Distribution
Different clouds for different regions:
Americas: AWS
Europe: Azure (GDPR compliance)
Asia: GCP (network performance)
Pros: Regulatory compliance, local performance Cons: Management overhead
Interview Question: Multi-Cloud Trade-offs
Q: "What are the main challenges of multi-cloud architectures?"
A:
| Challenge | Impact | Mitigation |
|---|---|---|
| Skill Requirements | Need expertise in 2-3 platforms | Invest in training, use abstraction layers |
| Data Egress Costs | Significant for large data movement | Minimize cross-cloud traffic, replicate strategically |
| Management Complexity | Different APIs, tools, consoles | Use Terraform, Kubernetes for abstraction |
| Security Consistency | Different IAM models, policies | Standardize with identity federation |
| Vendor Support | Blame game between providers | Clear service boundaries, SLAs |
Cloud Selection Decision Tree
Starting Point: What's your primary workload?
Enterprise/Microsoft-centric?
└── Azure (AD, Office 365, Dynamics)
Data/Analytics-heavy?
└── GCP (BigQuery, Vertex AI)
Web-scale/Microservices?
└── AWS (broadest services, largest community)
Hybrid/On-premises integration?
└── Azure (Arc, Stack) or AWS (Outposts)
Multi-region with premium networking?
└── GCP (global VPC, global LB)
Cost-sensitive compute?
└── GCP (sustained use discounts) or AWS (Savings Plans)
Abstraction Strategies
Infrastructure as Code
- Terraform: Multi-cloud IaC standard
- Pulumi: Programming language-based IaC
- Crossplane: Kubernetes-native cloud management
Container Orchestration
- Kubernetes: Portable across all clouds
- Anthos (GCP): Multi-cloud Kubernetes management
- Azure Arc: Multi-cloud management from Azure
Serverless Abstraction
- Knative: Portable serverless on Kubernetes
- KEDA: Event-driven autoscaling
- Dapr: Microservices runtime abstraction
Interview Tip: When discussing multi-cloud, always address the trade-offs between flexibility and complexity. The best answer depends on the organization's specific needs, not a one-size-fits-all recommendation.
This concludes the GCP & Azure Fundamentals module. Test your knowledge with the module quiz. :::