Multi-Cloud Guide

Multi-Cloud vs Hybrid Cloud

StrategyDefinitionBenefitChallenge
Multi-CloudUsing 2+ public clouds (AWS + GCP, etc.)Avoid vendor lock-in, leverage best servicesOperational complexity, skill overhead
Hybrid CloudOn-premises + one or more public cloudsData sovereignty, legacy integrationNetwork latency, consistent tooling
PolycloudDifferent services from different clouds per workloadBest-of-breed servicesData gravity, egress costs
Single CloudAll-in on one providerSimplicity, deep integration, discountsVendor lock-in, risk concentration

Cloud-Neutral Abstraction Layers

LayerTool / StandardWhat It Abstracts
InfrastructureTerraform / OpenTofu / PulumiVM, network, storage provisioning
ContainersKubernetes (any cloud)Compute scheduling, service discovery
Service MeshIstio / LinkerdTraffic management, mTLS, observability
StorageRook-Ceph, MinIO (S3-compatible)Object/block storage portability
CI/CDGitHub Actions, ArgoCD, TektonCloud-agnostic pipelines
ObservabilityOpenTelemetry, Prometheus, GrafanaMetrics, traces, logs across clouds
SecretsHashiCorp VaultCentralized secret management

Workload Placement Patterns

# Pattern 1: Best-of-breed services # - ML/AI workloads → GCP (Vertex AI, TPUs) # - Existing .NET / Azure AD → Azure # - Core infrastructure → AWS (most mature ecosystem) # Pattern 2: Active-active disaster recovery # - Same workload runs on AWS + GCP simultaneously # - Global load balancer (Cloudflare) distributes traffic # - Data replicated across clouds (expensive egress!) # Pattern 3: Arbitrage / cost optimization # - Use spot instances across clouds; pick cheapest # - Tools: Spot.io (Flexera), ProsperOps # Pattern 4: Regulatory / data residency # - EU customer data → Azure Europe (GDPR) # - US government → AWS GovCloud # - China market → separate cloud (Alibaba Cloud / Tencent) # Kubernetes federation (distribute across clouds) # kubectl ctx cluster-aws → deploy to AWS EKS # kubectl ctx cluster-gcp → deploy to GCP GKE # Kubefed / Flux Multi-cluster for automated federation