Choose the right cloud provider. Understand service mapping, pricing, strengths, and when to use each. Learn multi-cloud architecture patterns.
AWS, GCP, and Azure are like three grocery stores. AWS is the biggest and oldest (most selection, steepest learning curve). GCP is smaller but known for AI/analytics and simplicity. Azure is strong in enterprise/Windows environments. Each stores items in different aisles, charges differently, and has unique strengths. Most enterprises use all three.
| Use Case | AWS | GCP | Azure |
|---|---|---|---|
| VMs (long-running) | EC2 | Compute Engine | Virtual Machines |
| App hosting (managed) | Elastic Beanstalk | App Engine | App Service |
| Containers (serverless) | Fargate | Cloud Run | Container Instances |
| Serverless functions | Lambda | Cloud Functions | Functions |
| Kubernetes (managed) | EKS | GKE | AKS |
| Batch processing | Batch | Dataflow / Dataproc | Batch |
| Use Case | AWS | GCP | Azure |
|---|---|---|---|
| Object storage (S3-like) | S3 | Cloud Storage | Blob Storage |
| Block storage | EBS | Persistent Disk | Disk Storage |
| File storage (NFS) | EFS / FSx | Filestore | Azure Files |
| Data backup | AWS Backup | Cloud Backup | Backup |
| Cold archive | Glacier | Cloud Storage (Archive) | Archive Storage |
| Use Case | AWS | GCP | Azure |
|---|---|---|---|
| Relational SQL | RDS (MySQL/Postgres) | Cloud SQL | SQL Database |
| NoSQL key-value | DynamoDB | Firestore / Datastore | Cosmos DB (mode=Key-Value) |
| NoSQL document | DocumentDB (MongoDB-like) | Firestore (document-native) | Cosmos DB (mode=Mongo) |
| Distributed SQL | Aurora | Cloud Spanner | Cosmos DB (SQL) |
| Big data warehouse | Redshift | BigQuery | Synapse Analytics |
| In-memory cache | ElastiCache (Redis/Memcached) | Cloud Memorystore (Redis) | Redis Cache |
| Use Case | AWS | GCP | Azure |
|---|---|---|---|
| Virtual network | VPC | VPC | Virtual Network |
| Load balancer | ELB / ALB / NLB | HTTP(S) LB / Network LB | Load Balancer / Application Gateway |
| DNS | Route 53 | Cloud DNS | DNS |
| CDN | CloudFront | Cloud CDN | Front Door |
| VPN / Private link | Site-to-Site VPN / PrivateLink | Cloud Interconnect | VPN Gateway / Private Link |
| Use Case | AWS | GCP | Azure |
|---|---|---|---|
| Identity management | IAM (policy-based) | IAM (role-based) | RBAC (role-based, AD-native) |
| Audit logging | CloudTrail | Cloud Audit Logs | Azure Monitor / Activity Log |
| Secrets management | Secrets Manager | Secret Manager | Key Vault |
| Key management | KMS | Cloud KMS | Key Vault |
| Use Case | AWS | GCP | Azure |
|---|---|---|---|
| Data warehouse | Redshift | BigQuery (best-in-class) | Synapse Analytics |
| Data pipeline / ETL | AWS Glue | Dataflow (best), Dataproc | Data Factory |
| Machine Learning | SageMaker | Vertex AI (best for ML) | Machine Learning Service |
| Analytics | Athena | BigQuery | Synapse Analytics |
| Scenario | Best Choice | Why |
|---|---|---|
| Startup, no vendor preference | GCP | Cheaper, simpler, Cloud Run is magical for early devs. |
| Fortune 500, Windows-heavy, M365 integration | Azure | Built for enterprise. Hybrid support. Active Directory native. |
| Large data warehouse / BI analytics | GCP (BigQuery) | 50x faster than competitors for analytical queries. |
| Machine learning / deep learning | GCP (Vertex AI) | Best AutoML, pre-trained models, hosted notebooks. |
| Kubernetes-first company | GCP (GKE) | GKE is the most Kubernetes-native implementation. |
| Gaming, IoT, real-time analytics | AWS | DynamoDB, Kinesis, massive instance selection. Most optimized. |
| Hybrid on-prem + cloud | Azure | Only one with true on-prem cloud (Azure Stack). |
| Lowest latency for global users | AWS | Most regions + CloudFront as global CDN. |
| Cost-optimized, no preference | GCP | 25-35% cheaper. Sustained-use discounts. Preemptible VMs. |
Deploy critical app on AWS + GCP. Use DNS failover (Route 53 health checks) to route to whichever cloud is healthy. If AWS us-east-1 fails, traffic routes to GCP. Cost: Double infrastructure spending. Benefit: 99.999% uptime (5 nines).
Use GCP BigQuery for analytics (best warehouse), AWS RDS for transactional DB (mature), Azure for Windows workloads. Move data via pub/sub (Google Cloud Pub/Sub <→> AWS SNS). Each team owns their cloud.
Use Kubernetes on all clouds (GKE, EKS, AKS). Run same app everywhere. Avoid cloud-specific services (don't use BigQuery; use standard SQL instead). Costs more (lost benefits of each cloud) but gains flexibility.
AWS (largest, 33% market share), Microsoft Azure (enterprise, 23%), Google Cloud (data/AI focus, 11%). All three are viable. AWS has most services; GCP is cheapest; Azure is best for Windows/enterprise.
AWS Fargate. Both are serverless container platforms. Cloud Run is simpler, more elegant. Fargate is more powerful, more complex. For simple HTTP services, Cloud Run is easier.
Avoid vendor lock-in, resilience (if one cloud fails), and use the best tool for each job (BigQuery on GCP, DynamoDB on AWS). Most enterprises use 2-3 clouds.
DynamoDB (AWS): Key-value only, scales to millions of RPS, expensive at scale. Firestore (GCP): Document-native, good queries, cheaper. Cosmos DB (Azure): Multi-model, expensive, but global multi-master built-in. For startups: Firestore. For scale: DynamoDB. For Microsoft shops: Cosmos DB.
BigQuery is fully serverless (no infrastructure to manage). Redshift requires clusters (to tune performance). BigQuery scans terabytes in seconds using columnar storage + Zetabyte File System. Same query on Redshift takes minutes. BigQuery costs ~same but 50x faster. Redshift is legacy.
1. Map services (EC2 → Compute Engine, RDS → Cloud SQL, S3 → Cloud Storage). 2. Use Database Migration Service or Dataflow for data transfer. 3. Rewrite code for cloud-specific services (if using S3 SDK, change to Cloud Storage). 4. Test multi-cloud setup for 1-2 months (both clouds active). 5. Switch DNS to GCP. Most migrations take 3-6 months.
Migrate analytics to GCP BigQuery, keep operational systems on AWS. Use Change Data Capture (CDC) or ELT to stream data from AWS to BigQuery. Cost: $5k engineering + $500/month multi-cloud overhead. Benefit: Analytics 100x faster = faster insights = better product decisions. ROI: Positive in 2-3 months.
Active-Active on multiple clouds: AWS + GCP + Azure. Each cloud runs full app. Global load balancer (Google Cloud Load Balancer + AWS CloudFront + Azure Front Door, using external DNS to route). Replicate data across clouds (3-way replication). If one cloud fails, 2/3 still running. Cost: 3x infrastructure, but 99.999% uptime guaranteed (AWS alone: 99.95 = 21 hours downtime/year; 5 clouds = 26 seconds downtime/year).
Fortune 500 company: 60% workloads on Azure (Windows/SQL Server), 30% on AWS (legacy systems), 10% on GCP (data science). Each division owns cloud choice. Central team establishes ExpressRoute / AWS DirectConnect / Interconnect for private connectivity between clouds. Data flows via pub/sub bridges. No internet egress = cheaper + secure.
Early-stage SaaS: Runs on GCP for cost efficiency. But customer critical = risk. Deploy standby on AWS. GCP active (DNS weight 95%), AWS standby (weight 5%). Monthly failover test (5% of real users hit AWS to verify it works). If GCP fails, flip weight to 100% AWS. Users see <1 second DNS propagation delay.
Data company: GCP (BigQuery for BI), AWS (RDS for transactional), Azure (Power BI for dashboards). Data flow: Transactional data → AWS RDS → extract nightly → GCP BigQuery → Azure Power BI for dashboards. Each team owns their cloud. Total cost: $15k/month (vs $30k if all on one cloud).
No "best" cloud; choose based on your use case: