Multi-Cloud Deployment Setup (AWS + GCP / Azure)

Multi-Cloud Deployment Setup (AWS + GCP / Azure)

Recently we deployed an active infrastructure for a fintech startup: AWS for transactional workloads, GCP for ML inference. The client saved 200,000 rubles per month by redistributing load. Uptime over six months — 0. Deployment speed: 5 weeks, twice as fast as market average thanks to Terraform and GitOps.

Multi-cloud deployment means running your application across two or more cloud providers simultaneously. We have delivered over 20 projects on AWS, GCP, and Azure. Motivations vary: protection against a single provider's outage, regulatory requirements, access to unique services, or cost optimization. We offer a free project evaluation and propose an optimal architecture.

Scenarios for Multi-Cloud

Disaster Recovery (DR). Primary deployment in AWS, backup in GCP. Activated only during a full AWS outage. Minimal complexity: data replicated via managed services or custom logic.

Load Distribution. Different components in different clouds: web layer in AWS (closer to NA users), ML inference in GCP (cheaper TPU/GPU), data in Azure (for Microsoft-stack clients).

Full Active-Active. Both clouds handle traffic simultaneously. The most complex variant — requires state synchronization between clouds.

Network Connectivity Between Clouds

Direct traffic between AWS and GCP over public internet is unstable and insecure for data replication. Megaport/Equinix Cloud Exchange provides three times lower latency compared to site-to-site VPN.

For production, we recommend the first option.

Why Terraform is the Standard for Multi-Cloud

According to HashiCorp documentation, it manages resources across multiple clouds from a 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" { ... }

How to Set Up Terraform for Multi-Cloud

1. Install Terraform CLI version >=1.5.
2. Create a configuration file with required_providers for AWS and GCP.
3. Configure providers: specify regions, project IDs.
4. Describe resources for each cloud in separate modules.
5. Apply the configuration with terraform apply.

DNS Routing Between Clouds

Cloudflare Load Balancing works with endpoints in any cloud. Origin pools:

• AWS ALB (us-east-1) — weight 80
• GCP Cloud Load Balancing (us-central1) — weight 20

Failover: if the AWS pool degrades, Cloudflare redirects all traffic to GCP.

Route 53 + GCP Cloud DNS for active-active: CNAME with health check, TTL 60 seconds.

How to Synchronize Data Between Clouds

Object storage: rclone sync S3 → GCS or 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 — a single point for both clouds. Vault cluster is placed in one cloud, applications in both read from it via mTLS.

Kubernetes as a Unifying Layer

When using Kubernetes in both clouds (EKS + GKE), you can adopt a unified control plane via:

• Anthos (Google): manages clusters in GKE, EKS, AKS from one console
• Azure Arc: Microsoft's counterpart
• Rancher: open source multi-cluster management

A unified GitOps workflow (ArgoCD or Flux) deploys the same manifests to both clusters.

Challenges and How to Address Them

Different APIs and services. AWS S3 ≠ GCS (though similar). Use abstractions (boto3 + google-cloud-storage behind a common interface) or libcloud.

Different IAM models. Workload Identity Federation allows GCP services to obtain temporary AWS credentials via OIDC.

Observability. Centralized monitoring is mandatory. Datadog, Grafana Cloud, or OpenTelemetry Collector aggregate metrics from both clouds.

Latency. Cross-cloud requests add 50-150ms. Architecture must minimize synchronous cross-cloud calls.

What Is Included in Our Work

• Architectural diagram of the multi-cloud solution
• Terraform modules for AWS and GCP/Azure
• DNS routing and load balancing configuration
• Data synchronization and secrets management setup
• Observability configuration (logs, metrics, alerts)
• Operations documentation
• Team training
• One month of post-launch support

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 method)
• Observability + testing: 3–5 days

Total: 4–8 weeks for a full multi-cloud deployment. Our clients typically save 30% on cloud costs, which for large projects can reach hundreds of thousands of rubles per month.

We guarantee quality: our certified engineers have experience with AWS, GCP, and Azure. Contact us for a detailed discussion of your scenario. Order a turnkey multi-cloud deployment. Get a consultation from our architect today.

We regularly encounter a situation: "The site is not opening" at 3 a.m. — and it turns out that the VPS disk is full because nginx logs haven't been rotated for six months. Or the server went down under load on the day of an advertising campaign launch because the shared hosting had a limit of 50 concurrent connections. Setting up hosting and deployment is not about "where it's cheaper" but about what happens when something goes wrong. Our team helps avoid such incidents by designing infrastructure that accounts for real load patterns.

When to choose Vercel and Netlify?

Vercel is built for Next.js — deploy in one push, preview deployments for every PR, automatic CDN, Edge Functions, ISR without configuration. For frontend projects and JAMstack, it's the optimal choice: no operational overhead, time-to-deploy measured in minutes.

Real limitations: Vercel Serverless Functions run in us-east-1 by default (latency for Europe +80–100ms), Function timeout 300 seconds on Pro, Bandwidth 1TB/month on Pro. For heavy backend, you need workers or a separate server.

Netlify is closer to static sites and Edge Functions based on Deno Deploy. Build minutes are the main limitation on the free tier.

Why is Docker the foundation of predictable deployment?

"It works on my machine" — classic. Docker solves this through environment containerization. But a bad Dockerfile creates new problems.

A typical mistake: copying everything into the image without .dockerignore, resulting in an 800MB image instead of 80MB. node_modules inside the image weighs as much. Correct approach: multi-stage build.

FROM node:20-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM node:20-alpine AS runner
WORKDIR /app
COPY --from=builder /app/.next ./.next
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./package.json
EXPOSE 3000
CMD ["npm", "start"]

Final image: 180MB instead of 1.2GB. CI build time is reduced due to layer caching — if package.json hasn't changed, the layer with npm ci is taken from cache.

Docker Compose for local development and simple production scenarios: application + PostgreSQL + Redis in one configuration. For production on a single server, it's a perfectly viable option if there's no requirement for horizontal scaling.

More about containerization — Wikipedia: Docker.

How to set up Nginx as a reverse proxy?

Nginx in front of the application is standard for VPS and dedicated servers. Main functions: SSL termination, gzip, static files, rate limiting, upstream load balancing.

A configuration often done incorrectly: worker_processes auto — number of processes equals CPU count. worker_connections 1024 — that's 1024 per worker process. With 4 CPUs and 1024 connections = 4096 concurrent connections. For a high-traffic site, you need worker_connections 4096 and set keepalive_timeout 65.

For static assets with hash in the filename:

location ~* \.(js|css|woff2|png|webp)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}

immutable tells the browser: don't revalidate this file even on hard refresh. This only works correctly with content-hashed filenames (which Vite/webpack do by default). Documentation — Wikipedia: Nginx.

AWS: flexibility and complexity

EC2 + Auto Scaling Group — classic for horizontal scaling. AMI with pre-installed application, Launch Template, ASG with min/desired/max instances, Application Load Balancer. When CPU > 70% for 3 minutes — scale out, when CPU < 30% for 15 minutes — scale in. Health check via ALB removes unhealthy instances from rotation.

ECS Fargate — containers without managing EC2. Deploy a Docker image, specify CPU/memory (512 CPU units = 0.5 vCPU, from 512MB memory), Fargate launches it. More expensive than Lambda, but no cold start and no timeout limitations. Suitable for long-running processes, WebSocket servers, heavy workers.

RDS for PostgreSQL with Multi-AZ: automatic failover in 1–2 minutes when primary fails. Read Replicas for scaling reads. RDS Proxy for connection pooling — Lambda functions cannot hold long-term connections, the proxy buffers this.

Kubernetes: when it is justified

K8s adds significant operational complexity. Justified when: multiple teams deploy independent services, fine-grained resource allocation per service is needed, canary deployments and blue/green without downtime are required.

AWS EKS, GKE, or managed k8s from Hetzner (cheaper). Helm charts for standard services. Horizontal Pod Autoscaler based on CPU and custom metrics (RPS via Prometheus).

For most startups and medium-sized projects, Kubernetes is overkill. ECS or Fly.io provide 80% of the capabilities with 20% of the operational complexity.

Monitoring and alerting

A server without monitoring is waiting for an incident. Minimal stack: Prometheus + Grafana (or Grafana Cloud for managed), alerting on disk > 80%, memory > 85%, CPU > 90% over 5 minutes, error rate > 1%. Uptime via Better Uptime or Upptime (self-hosted).

Logs: Loki + Grafana or CloudWatch Logs Insights. Structured JSON logs (winston, pino) are mandatory — otherwise, log searching becomes a pain.

What is included in hosting setup

• Audit of current infrastructure and load profiling
• Selection of target architecture (VPS, AWS, serverless, Kubernetes)
• Setting up CI/CD pipeline (GitHub Actions, GitLab CI) with automatic deployment
• IaC via Terraform or Pulumi (infrastructure as code)
• Configuration of Nginx, SSL certificates, HTTP/2, brotli
• Monitoring and alerting (Prometheus + Grafana, PagerDuty)
• Documentation of runbooks and team training

Additionally, contact us if you need migration from current hosting or integration with external services.

Work process

1. Audit of current infrastructure (2–5 days)
2. Selection of target architecture with load and budget justification (1–3 days)
3. Setting up CI/CD pipeline (GitHub Actions, GitLab CI) (2–5 days)
4. IaC via Terraform or Pulumi (3–10 days)
5. Setting up monitoring and alerting (2–5 days)
6. Documentation of runbooks and team training (1–3 days)

Our experience — 7 years on the market, over 50 projects, guarantee of operability after deployment.

Timeline

• Basic deployment on VPS with Docker + Nginx + CI/CD: 1–2 weeks.
• Setting up AWS infrastructure with Auto Scaling, RDS, CDN: 3–6 weeks.
• Migration to EKS from scratch: 6–12 weeks.
• Setting up Vercel/Netlify for JAMstack: 3–5 days.

The cost is calculated individually depending on complexity and scope of work. Get a consultation — we'll evaluate your architecture in one day.