⚙️ Compute Services - Advanced Comparison

Enterprise Considerations

Performance Benchmarking

# AWS EC2 - CPU optimized
aws ec2 describe-instance-types \
  --filters "Name=instance-type,Values=c6i.*" \
  --query 'InstanceTypes[*].[InstanceType,VCpuInfo.DefaultVCpus,MemoryInfo.SizeInMiB]'

# GCP - Machine types comparison
gcloud compute machine-types list --filter="zone:us-central1-a"

# Azure - SKU information
az vm list-skus --location eastus --query "[].name" -o tsv

Cost Optimization Patterns

Reserved Instances (AWS/Azure)

1-year commitment: ~40% discount
3-year commitment: ~60% discount
Ideal for: Baseline workloads

Sustained Use Discounts (GCP)

Automatic discounts after 25% monthly usage
No upfront commitment
Ideal for: Variable workloads

Spot/Preemptible Instances

AWS Spot: ~70% discount, can be interrupted
GCP Preemptible: ~80% discount, 30min max lifetime
Azure Spot: ~90% discount
Use case: Batch processing, non-critical workloads

Multi-Cloud Strategies

Workload Placement Decision Matrix

                 Control  |  Management  |  Cost  |  Serverless
AWS EC2          10/10    |    1/10      |  6/10  |    N/A
AWS ECS          8/10     |    5/10      |  7/10  |    N/A
AWS Lambda       3/10     |   10/10      |  8/10  |   9/10

GCP CE           10/10    |    1/10      |  7/10  |    N/A
GCP Cloud Run    4/10     |   10/10      |  9/10  |   10/10
GCP GKE          8/10     |    7/10      |  6/10  |    N/A

Azure VMs        10/10    |    2/10      |  5/10  |    N/A
Azure AKS        8/10     |    7/10      |  5/10  |    N/A
Azure Functions  2/10     |   10/10      |  8/10  |   9/10

Firebase FCN     2/10     |   10/10      |  8/10  |   9/10

Vendor Lock-in Mitigation

Containerization

• All providers support Docker

• Use Kubernetes for portability

• Avoid provider-specific services

Infrastructure as Code

# Terraform works across all providers
resource "aws_instance" "example" {
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t3.micro"
}

resource "google_compute_instance" "example" {
  name         = "test"
  machine_type = "e2-micro"
  zone         = "us-central1-a"
}

resource "azurerm_virtual_machine" "example" {
  name                  = "test-vm"
  location              = "East US"
  resource_group_name   = azurerm_resource_group.example.name
  vm_size               = "Standard_B1s"
}

Performance Optimization

Network Performance

AWS:

• Placement groups for low latency

• Enhanced networking (up to 100 Gbps)

• Nitro system for better I/O

GCP:

• Global load balancing native

• Network Service Tiers (premium vs standard)

• Committed use discounts on network

Azure:

• Accelerated networking (up to 30 Gbps)

• Azure Proximity Placement Groups

• Global load balancing

Memory and CPU Trade-offs

AWS Graviton:  ARM-based, 20% cheaper
GCP N2:        Memory optimized, lower cost
Azure Ev3:     Memory intensive workloads

Scaling Strategies

Horizontal Scaling

AWS Auto Scaling Group

aws autoscaling create-auto-scaling-group \
  --auto-scaling-group-name my-asg \
  --launch-configuration my-lc \
  --min-size 1 \
  --max-size 10 \
  --desired-capacity 3 \
  --load-balancer-names my-lb

GCP Instance Group

gcloud compute instance-groups managed create my-group \
  --template=my-template \
  --size=3 \
  --zone=us-central1-a

Azure VMSS

az vmss create \
  --resource-group myResourceGroup \
  --name myScaleSet \
  --image UbuntuLTS \
  --admin-username azureuser

Vertical Scaling Considerations

• Downtime required for VMs

• Container orchestration handles automatically

• Serverless scales implicitly

Cost Analysis Framework

Total Cost of Ownership (TCO)

Compute Cost = Hourly Rate × Hours × Discount
Storage Cost = GB × Replication Factor × Region Premium
Network Cost = GB Transfer × Direction (In/Out)
Support Cost = Varies by tier (all offer free tier)

Cost Attribution

AWS Cost Explorer

Tagging strategy → Cost center allocation
Reserved capacity planning → 40-60% savings

GCP Billing Export

BigQuery export → Custom cost analysis
Anomaly detection → Alert on unexpected charges

Azure Cost Management

Budget alerts → Prevent overspend
Reservation optimization → Automated recommendations

Disaster Recovery & HA

Multi-Region Strategies

Active-Active

• Traffic to multiple regions

• Complex synchronization

• Higher cost but better availability

Active-Passive

• Failover on primary failure

• Lower cost, acceptable RTO/RPO

• Common for most workloads

Backups and Recovery

AWS: Snapshots → AMI → Deploy
GCP: Images → Instances → Verify
Azure: Managed Images → VM → Test

Monitoring and Observability

Provider Tools

AWS CloudWatch

• Basic monitoring: free

• Detailed monitoring: charged

• Custom metrics: $0.10/month

GCP Cloud Monitoring

• First 150MB/month: free

• Then: $0.25 per MB

Azure Monitor

• Unified monitoring platform

• Log Analytics: $0.70 per GB ingestion

Firebase Performance Monitoring

• Free tier included

• Automatic SDK integration

• Real user monitoring

Key Takeaways

• **Compute choice depends on:** Control requirements, team expertise, cost sensitivity

• **Serverless wins on:** Operational overhead, scalability, cost per transaction

• **VMs win on:** Control, predictable performance, legacy support

• **Containers balance:** Control and management, portable across providers

• **Cost is dynamic:** Monitor and optimize continuously with provider tools

• **Multi-cloud risk:** Significant complexity without proper abstraction layers

• **Lock-in is real:** Plan exit strategy before full migration

• **Benchmarking essential:** Test YOUR workload on each platform

Recommendations for Different Scenarios

Startup (Cost Sensitive)

→ GCP Cloud Run + Cloud Storage + Firestore

Enterprise (Control Important)

→ AWS EKS + EC2 + RDS

Microsoft Ecosystem

→ Azure App Service + SQL Database + Functions

Real-time Applications

→ Firebase Cloud Functions + Realtime Database

Complex Microservices

→ Platform-agnostic: Kubernetes (EKS/GKE/AKS)

Final Thought: The best cloud is the one that solves your specific problem at the lowest cost with minimal operational burden.