Designing a Multi-Cloud Strategy

Multi-cloud distributes workloads across multiple cloud providers for resilience, cost optimization, and avoiding vendor lock-in.

Multi-Cloud Motivations

Resilience:       If AWS goes down, GCP handles traffic
Cost:             Use cheapest provider per service type
Regulation:       Data must stay in specific regions/clouds
Best-of-breed:    GCP ML, AWS Lambda, Azure DevOps
Negotiation:      Vendor competition drives better pricing

Cloud Lock-In Risks

High lock-in services:
  AWS Lambda (function format)
  DynamoDB (proprietary API)
  BigQuery (Google-specific SQL)
  Azure CosmosDB (proprietary)

Low lock-in (portable):
  Kubernetes (runs anywhere)
  PostgreSQL (standard SQL)
  Kafka (self-hosted or managed)
  S3-compatible storage (MinIO, Wasabi)

Abstraction Layer Pattern

Application code
    ↓
Abstraction Layer (storage, queue, compute APIs)
    ↓
Cloud Provider Adapters
    ├── AWS adapter
    ├── GCP adapter
    └── Azure adapter

Switch providers: Only change adapter, not app code

Data Gravity Problem

Challenge: Data moves to compute, not compute to data

Moving 100TB from AWS to GCP:
  Egress cost: $9,000 (AWS charges $0.09/GB)
  Time: Days to weeks

Solutions:
  Minimize cross-cloud data movement
  Use globally distributed databases (Spanner, CockroachDB)
  Keep related compute + data in same cloud

Kubernetes as Portability Layer

Kubernetes cluster on AWS → Same manifests on GCP

Managed Kubernetes:
  AWS: EKS
  GCP: GKE
  Azure: AKS

Deployments, Services, ConfigMaps: Identical
Differences: Load balancer class, storage classes
Tool: Crossplane (manage cloud resources via K8s)

Traffic Routing

Global load balancing:
  Route 70% to AWS, 30% to GCP
  Failover: If AWS latency > 500ms, route to GCP

Tools:
  Cloudflare (multi-cloud load balancing)
  Route53 (latency-based routing)
  NS1 (advanced multi-cloud routing)

Cost Optimization Across Clouds

Spot/Preemptible instances:
  AWS Spot: 70-90% discount, can be interrupted
  GCP Spot: 60-91% discount

Reserved capacity:
  Commit 1-3 years for 40-60% discount
  Mix: 60% reserved, 40% spot

Cost allocation:
  Tag all resources by team/service
  Chargeback to business units
  Auto-terminate unused resources

Conclusion

Multi-cloud is worth the complexity for large organizations. Kubernetes for portability, open-source data technologies for freedom, and abstraction layers prevent deep lock-in.