How to Seamlessly Architect a Multi-Cloud Strategy from AWS to Google Cloud
Most cloud migration talks focus on moving to one platform. Let's flip the script and explore architecting for a multi-cloud future that starts with AWS and Google Cloud, enabling dynamism over dependency.
In today’s fast-changing digital landscape, organizations want flexibility, resilience, and freedom from vendor lock-in. Moving workloads from AWS to Google Cloud — or better yet, architecting your infrastructure across both — can be a game changer. But how do you do it seamlessly without creating operational headaches?
This post walks through practical tips and strategies to design a multi-cloud architecture that leverages the best of AWS and Google Cloud while ensuring smooth workload transitions.
Why Build a Multi-Cloud Strategy from AWS to Google Cloud?
AWS is the market leader with vast services and global reach; Google Cloud stands out with Kubernetes expertise, AI/ML capabilities, and strong data analytics tools. Using both strategically lets you:
- Avoid vendor lock-in: Change providers or distribute workloads based on price/performance.
- Leverage unique strengths: Run AI-heavy pipelines on GCP while using AWS for mature serverless offerings.
- Increase resilience: Cross-cloud disaster recovery and failover for critical systems.
- Optimize cost: Shift workloads dynamically depending on current pricing/promotions.
Step 1: Assess Your Current AWS Workloads
Begin by auditing what you’re running in AWS:
- Identify tightly coupled components vs modular microservices.
- Catalog dependencies (databases, queues, storage).
- Understand network architecture and security groups.
- Note compute types (EC2 instances, Lambda functions).
The goal is knowing which apps can be migrated as-is, which need redesign, and which perhaps should remain AWS-only.
Step 2: Choose Your Migration Approach
Lift-and-shift is fastest but might not use GCP features fully.
Refactor/replatform means modifying apps—often ideal for long-term success.
Examples:
- Move EC2 VMs to Compute Engine or GKE (Google Kubernetes Engine) clusters.
- Convert RDS instances to Cloud SQL or Cloud Spanner.
- Replace SQS queues with Pub/Sub topics.
If you use containerized workloads already (e.g., Docker on ECS), transitioning to GKE can smooth migration.
Step 3: Design Your Networking & Identity Model
Networking between clouds is critical:
- Use VPN tunnels or dedicated interconnects for secure communication between AWS VPCs and GCP VPCs.
- Establish consistent IP ranges or use DNS names for cross-cloud services.
- For identity, consider federating identities with a centralized provider like Okta or configure IAM roles in both clouds linked to your identity provider.
This will make hybrid cloud applications more manageable by avoiding fragmented auth systems.
Step 4: Manage Data Consistency Across Clouds
Data synchronization is often the trickiest part.
If your application requires near real-time consistency:
- Look into managed replication tools like Google Cloud’s Database Migration Service or open-source tools such as Debezium.
- For object storage, you might sync data using S3-compatible gateways or third-party solutions (e.g., Rclone).
For eventual consistency models, implement event-driven patterns where changes in one cloud trigger updates in the other via message queues like Amazon SNS → Google Pub/Sub bridges.
Step 5: Embrace Infrastructure as Code & CI/CD Pipelines
To maintain parity across environments:
- Use Terraform modules targeting both AWS and GCP resources — write multi-cloud configs where possible.
- Leverage CI/CD pipelines that deploy simultaneously or conditionally across providers (GitHub Actions, Jenkins pipelines).
Example snippet in Terraform defining compute instance on both clouds:
resource "aws_instance" "app_server" {
count = var.cloud_provider == "aws" ? 1 : 0
ami = var.aws_ami
instance_type = "t3.micro"
}
resource "google_compute_instance" "app_server" {
count = var.cloud_provider == "gcp" ? 1 : 0
name = "gcp-app-server"
machine_type = "f1-micro"
zone = var.gcp_zone
}
Passing cloud_provider variable allows testing deployment on either platform.
Step 6: Monitor & Optimize Across Clouds
Centralize logging and monitoring via platforms that integrate multiple clouds:
- Use Prometheus + Grafana with exporters deployed in both environments.
- Send logs to centralized ELK stack or managed services like Datadog that support multi-cloud visibility.
Analyze usage patterns and automate scaling or workload shifting dynamically based on performance/cost metrics.
Example Use Case: Migrating a Web App Backend from AWS Lambda to Google Cloud Functions
Let’s say your web app backend uses event-driven serverless functions heavily on Lambda triggered by API Gateway requests and DynamoDB streams.
To migrate partially without downtime:
- Replicate DynamoDB tables to Bigtable via Data Pipeline tools.
- Recreate API endpoints in Cloud Endpoints with corresponding Cloud Functions using Node.js runtime compatible with Lambda logic.
- Update DNS/Route53 gradually redirecting traffic to GCP endpoints while monitoring error rates.
This staged approach lets you pilot test while still running production resiliently on AWS until ready for full cutover.
Final Thoughts
Architecting a multi-cloud strategy isn’t just IT overhead—it’s strategic agility allowing you to:
- Choose best-of-breed services dynamically
- Avoid single points of failure
- Optimize costs aggressively
- Future-proof against vendor lock-in risks
Starting from an existing AWS footprint toward integrating or migrating workloads into Google Cloud can be challenging but rewarding. By carefully planning migration approaches, networking, data syncing, automation, and monitoring upfront—your cloud environment becomes truly flexible across providers.
The key is building repeatable infrastructure-as-code processes that enable you to toggle workloads between clouds instead of being stuck choosing just one.
Have you started your multi-cloud journey? Feel free to share challenges and tips below!