Setting Up Multi-Cloud Deployment (AWS + GCloud / Azure)
Multi-cloud deployment is simultaneous use of two or more cloud providers to run application. Motivation varies: protection from one provider outage, regulatory requirements, use of unique services, price optimization. Each motivation suggests different architecture.
Multi-Cloud Scenarios
Standby (DR). Primary deployment in AWS, standby in GCP. Activates only on complete AWS outage. Minimal complexity: data replicates via managed services or custom logic.
Load Distribution. Different components in different clouds: web tier in AWS (closer to NA users), ML inference in GCP (TPU/GPU better pricing), data in Azure (for Microsoft-stack clients).
Full Active-Active. Both clouds accept traffic simultaneously. Most complex variant — state synchronization between clouds.
Network Connectivity Between Clouds
Direct traffic between AWS and GCP over public internet — unstable and unsafe for data replication.
Megaport / Equinix Cloud Exchange: physical connection through traffic exchange point. Minimal latency, stable throughput. Optimal for production.
AWS Direct Connect + GCP Interconnect to single exchange point: more complex, but provides dedicated channel without public internet dependency.
Site-to-site VPN: IPSec tunnels between VPC (AWS) and VPC Network (GCP). Faster to setup, less reliable, limited throughput.
Terraform for Multi-Cloud Deployment
Terraform manages resources in multiple clouds from single configuration:
terraform {
required_providers {
aws = { source = "hashicorp/aws", version = "~> 5.0" }
google = { source = "hashicorp/google", version = "~> 5.0" }
}
}
provider "aws" {
region = "us-east-1"
}
provider "google" {
project = "my-project"
region = "us-central1"
}
# AWS: primary cluster
resource "aws_eks_cluster" "main" { ... }
# GCP: DR cluster / ML components
resource "google_container_cluster" "dr" { ... }
DNS Routing Between Clouds
Cloudflare Load Balancing — works with endpoints in any clouds. Origin pools:
- AWS ALB (us-east-1) — weight 80
- GCP Cloud Load Balancing (us-central1) — weight 20
Failover: on AWS pool degradation — Cloudflare redirects all traffic to GCP.
Route 53 + GCP Cloud DNS in active-active: CNAME with health check, TTL 60 seconds.
Data Synchronization
Object storage: rclone sync S3 → GCS or vice versa. Or application writes to both simultaneously (dual write pattern).
Databases: CockroachDB, YugabyteDB, or Spanner (GCP only) natively support multi-region/multi-cloud via Raft replication. Alternative — Debezium CDC from PostgreSQL to Kafka, consumer writes to GCP DB.
Secrets: HashiCorp Vault — single point for both clouds. Vault cluster in one cloud, applications in both read via mTLS.
Kubernetes as Unifying Layer
When running Kubernetes in both clouds (EKS + GKE), use single control plane via:
- Anthos (Google): manages clusters in GKE, EKS, AKS from single console
- Azure Arc: Microsoft equivalent
- Rancher: open source multi-cluster management
Single GitOps workflow (ArgoCD or Flux) deploys same manifests to both clusters.
Complexities and How to Handle
Different APIs and services. AWS S3 ≠ GCS (though similar). Use abstractions (boto3 + google-cloud-storage behind common interface) or libcloud.
Different IAM models. Workload Identity Federation lets GCP services get temporary AWS credentials via OIDC.
Observability. Centralized monitoring mandatory. Datadog, Grafana Cloud, or OpenTelemetry Collector aggregating metrics from both clouds.
Latency. Inter-cloud requests add 50-150ms. Architecture should minimize synchronous cross-cloud calls.
Implementation Timeline
- Network connectivity (VPN or Interconnect) — 3-7 days
- Terraform modules for both clouds — 5-10 days
- DNS failover + load balancing — 2-3 days
- Data synchronization — 5-14 days (depends on chosen method)
- Observability + testing — 3-5 days
Total: 4-8 weeks for complete multi-cloud deployment.