Multi-Cloud Architecture

Service Comparison

Compute

Storage

Database

Architecture Patterns

Single Provider with DR -- Primary workload in one cloud, DR in another. Database replication + automated failover.

Best-of-Breed -- AI/ML on GCP, Enterprise apps on Azure, General compute on AWS. Pick strengths per provider.

Geographic Distribution -- Serve from nearest region, data sovereignty compliance, global load balancing.

Cloud-Agnostic Abstraction -- Portable stack to reduce lock-in:

Networking

Connection Options

VPN vs Dedicated Connection Decision

Default: Start with VPN, upgrade to dedicated when bandwidth or latency demands it.

Hub-and-Spoke Topology

On-Premises Datacenter
         |
    VPN / Direct Connect
         |
    Transit Gateway (AWS) / vWAN (Azure) / Cloud Router (GCP)
    +-- Production VPC/VNet
    +-- Staging VPC/VNet
    +-- Development VPC/VNet

BGP Essentials

• On-prem router advertises internal CIDRs (e.g., 10.0.0.0/8) with private ASN (64512-65534)
• Cloud-side ASNs: AWS default 64512, Azure fixed 65515, GCP configurable
• Always run dual tunnels for HA -- active/active with ECMP or active/passive
• Monitor: tunnel status, BGP session state, packet loss, latency, bytes in/out

Cost Optimization

Pricing Models

Tagging Strategy (Required Tags)

Cost Optimization Checklist

• Tag all resources with required tags above
• Delete unused resources (unattached disks, idle LBs, old snapshots, unassociated EIPs)
• Right-size instances based on utilization (use provider advisors/recommenders)
• Reserved capacity for steady-state workloads; spot/preemptible for fault-tolerant
• Implement auto-scaling with appropriate cooldowns
• Storage lifecycle policies: hot -> warm -> cold -> archive
• Set budget alerts at 50%, 80%, 100% thresholds
• Enable cost anomaly detection
• Optimize data transfer (same-AZ where possible, VPC endpoints, CDN)
• Add caching layers to reduce compute/DB load

Cost Tools

• AWS: Cost Explorer, Compute Optimizer, Cost Anomaly Detection
• Azure: Cost Management, Advisor
• GCP: Cost Management, Recommender
• Multi-cloud: CloudHealth, Cloudability, Kubecost

Migration Strategy

1. Assessment -- Inventory workloads, map dependencies, estimate costs, identify compliance constraints
2. Pilot -- Select low-risk workload, implement, validate, document lessons
3. Migration -- Incremental moves, dual-run period, automated testing, rollback plan per workload
4. Optimization -- Right-size, adopt cloud-native services, implement cost governance

Gotchas and Anti-Patterns

• Lift-and-shift everything -- Re-platform or re-architect where ROI justifies it
• Ignoring egress costs -- Data transfer between clouds/regions adds up fast; design data gravity around primary provider
• Multi-cloud for the sake of it -- Real multi-cloud adds operational complexity; have a concrete reason (DR, best-of-breed, compliance)
• No abstraction layer -- Without Terraform/K8s, multi-cloud becomes multi-headache
• Skipping tagging -- Impossible to optimize costs or enforce governance without consistent tags
• Single tunnel -- Always deploy redundant VPN tunnels; single tunnel = single point of failure
• Overlapping CIDRs -- Plan IP address space across all environments upfront; retrofitting is painful
• No network monitoring -- Hybrid connectivity issues are invisible without proactive tunnel/BGP monitoring