Ahmed Rizawan

Have you ever tried juggling while riding a unicycle? That’s what managing multiple cloud providers felt like when I first started working with both AWS and GCP simultaneously. After five years of wrestling with multi-cloud architectures, I’ve learned some valuable lessons – often the hard way.

Picture this: It’s 3 AM, and you’re trying to debug why your cross-cloud data replication is failing while your team is frantically messaging about cost spikes in both platforms. Sound familiar? Let’s dive into the real-world challenges and practical solutions I’ve discovered while running workloads across AWS and GCP.

The Multi-Cloud Reality Check

First things first – despite what some vendors might tell you, running multiple clouds isn’t just about signing up for different services and calling it a day. It’s more like maintaining two separate houses in different countries, each with its own rules, maintenance requirements, and quirks.

Identity and Access Management: The Foundation

One of the biggest challenges I faced was managing identities across both clouds. AWS has IAM, GCP has IAM (yes, same name, different systems), and they don’t exactly play nice together out of the box. Here’s how we tackled this:

# AWS IAM Role example
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::my-bucket",
                "arn:aws:s3:::my-bucket/*"
            ]
        }
    ]
}

For GCP, the equivalent looks quite different:

# GCP IAM Policy
bindings:
- members:
  - serviceAccount:[email protected]
  role: roles/storage.objectViewer
  condition: null

Networking: The Great Connector

Getting your clouds to talk to each other is crucial. We implemented a hub-and-spoke network topology using Cloud Interconnect and AWS Direct Connect. Here’s a simplified view of our setup:

graph LR
    A[AWS VPC] --Direct Connect--> C{On-Prem Hub}
    B[GCP VPC] --Cloud Interconnect--> C
    C --Routes--> D[Internal Services]

Cost Management: The Budget Balancing Act

Managing costs across multiple clouds is like trying to track expenses in different currencies. Here are some hard-learned lessons:

• Set up separate billing alerts for each cloud provider with different thresholds
• Use tagging strategies that work across both platforms
• Implement automated cost optimization tools for each platform
• Regular audit of unused resources in both clouds

Monitoring and Observability: The Full Picture

We learned that native monitoring tools don’t give us the full picture. We ended up building a unified monitoring solution that pulls metrics from both clouds. Here’s a basic example of how we aggregate logs:

def aggregate_cloud_metrics():
    # AWS CloudWatch metrics
    aws_metrics = cloudwatch.get_metric_data(
        MetricDataQueries=[
            {
                'Id': 'm1',
                'MetricStat': {
                    'Metric': {
                        'Namespace': 'AWS/EC2',
                        'MetricName': 'CPUUtilization'
                    },
                    'Period': 300,
                    'Stat': 'Average'
                }
            }
        ]
    )
    
    # GCP Monitoring metrics
    gcp_metrics = monitoring_v3.TimeSeriesServiceClient()
    resource_name = f"projects/{project_id}"
    interval = monitoring_v3.TimeInterval(
        start_time=start_time,
        end_time=end_time
    )

Disaster Recovery: The Safety Net

Operating across multiple clouds actually gave us an advantage in disaster recovery. We implemented a cross-cloud DR strategy that looks something like this:

• Primary workloads in AWS with hot standby in GCP
• Database replication across clouds using cloud-native services
• Regular failover testing between providers
• Automated health checks and failover triggers

Security: The Common Ground

Security in a multi-cloud environment requires a unified approach. We standardized on:

# Example of unified security scanning script
#!/bin/bash

# Scan AWS resources
aws_scan() {
    aws securityhub get-findings --filters '{"RecordState": [{"Value": "ACTIVE"}]}'
}

# Scan GCP resources
gcp_scan() {
    gcloud security-center findings list --organization=$ORG_ID
}

# Aggregate results
aggregate_findings() {
    # Custom aggregation logic
    echo "Aggregating security findings..."
}

Automation: The Great Equalizer

Automation became our best friend. We use Infrastructure as Code (IaC) to maintain consistency across both clouds. Here’s a snippet of our Terraform setup:

# AWS Provider
provider "aws" {
  region = "us-west-2"
}

# GCP Provider
provider "google" {
  project = "my-project"
  region  = "us-central1"
}

# Common tags/labels
locals {
  common_tags = {
    environment = "production"
    managed_by = "terraform"
  }
}

Looking back, running a multi-cloud infrastructure has been both challenging and rewarding. The key is to embrace the complexity while working to simplify it through automation, standardization, and clear operational procedures.

What’s your experience with multi-cloud environments? Have you found any creative solutions to these common challenges? I’d love to hear your stories and solutions in the comments below.