Why Multi-Cloud?
When I started my cloud journey, I was an Azure purist. Microsoft stack, Azure services, everything in one ecosystem. It made sense. Then reality hit.
Different clients had different requirements:
- Government clients mandated specific cloud providers
- Some regions only had certain providers available
- Cost optimization sometimes required mixing providers
- Specific services were better on certain platforms
Over the years, I have deployed systems across Azure, AWS, G42 Cloud, PureCS, Oracle Cloud, and GCP. Here is what I have learned about making multi-cloud work.
The Multi-Cloud Reality Check
It is Not About Using Everything
Multi-cloud does not mean using all clouds simultaneously for every project. It means:
- Having the capability to deploy on different platforms
- Choosing the right cloud for each use case
- Avoiding vendor lock-in where it matters
- Meeting client and regulatory requirements
The Hidden Costs
Before going multi-cloud, understand the costs:
- Skills: Your team needs to know multiple platforms
- Tooling: Different clouds, different tools (or invest in cloud-agnostic tooling)
- Networking: Cross-cloud communication adds complexity and latency
- Security: Each cloud has different security models
- Operations: Monitoring, alerting, and incident response multiply
Cloud Comparison: My Experience
Azure
Best for:
- .NET workloads (native integration)
- Enterprise clients already on Microsoft 365
- Hybrid cloud scenarios
- AI/ML with Azure ML and Cognitive Services
Watch out for:
- Portal can be confusing
- Pricing complexity
- Some services have regional limitations
AWS
Best for:
- Widest service selection
- Serverless (Lambda is mature)
- Container orchestration (EKS, Fargate)
- Global reach
Watch out for:
- Cost can spiral quickly
- IAM complexity
- Service naming is not intuitive
GCP
Best for:
- Kubernetes (GKE is excellent)
- Data analytics and BigQuery
- Machine learning (TensorFlow integration)
- Competitive pricing
Watch out for:
- Smaller market share means fewer resources
- Some enterprise features lag behind
G42 Cloud and Regional Providers
Best for:
- Data residency requirements
- Government and regulated industries
- Middle East deployments
Watch out for:
- Smaller service catalog
- Less documentation and community support
- Different SLAs than hyperscalers
Architecture Patterns for Multi-Cloud
Pattern 1: Cloud-Agnostic Core
Build your core application to be cloud-agnostic:
[Application Layer] - Business logic, no cloud dependencies
|
[Abstraction Layer] - Interfaces for cloud services
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[Cloud Adapters] - Azure, AWS, GCP implementations
This allows swapping cloud providers without rewriting application code.
Pattern 2: Best-of-Breed Services
Use each cloud for what it does best:
- Compute: Where your team has expertise
- AI/ML: Azure Cognitive Services or AWS SageMaker
- Data: BigQuery for analytics, managed databases where needed
- CDN: Cloudflare or cloud-native CDN
Connect via APIs with proper security and latency considerations.
Pattern 3: Active-Passive Multi-Cloud
Primary workloads on one cloud, disaster recovery on another:
- Primary: Full application stack
- Secondary: Data replication, minimal compute, ready to scale
- Failover: DNS-based or load balancer routing
This provides true disaster recovery without the cost of running everything twice.
Kubernetes: The Multi-Cloud Enabler
Kubernetes has been the key enabler for our multi-cloud strategy:
- Same deployment manifests across clouds
- Consistent networking model
- Portable observability stack
- Cloud-agnostic CI/CD pipelines
Our K8s Setup
- Development: Local Kubernetes (Docker Desktop or minikube)
- Staging: Cloud-managed Kubernetes (AKS, EKS, or GKE)
- Production: Managed Kubernetes with multi-zone deployment
Key Kubernetes Practices
- Avoid cloud-specific features in your K8s configs where possible
- Use Helm charts for templating across environments
- Implement GitOps (ArgoCD or Flux) for consistent deployments
- Standardize on observability (Prometheus, Grafana, ELK stack)
CI/CD for Multi-Cloud
Our pipeline structure:
Code Commit
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[Build & Test] - Cloud-agnostic
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[Security Scan] - Container scanning, SAST
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[Build Container] - Push to registry
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[Deploy to Target] - Environment-specific config
Tools That Work Across Clouds
- GitHub Actions or GitLab CI: Platform-agnostic pipelines
- Terraform: Infrastructure as code for any cloud
- Docker: Consistent container builds
- Helm: Kubernetes package management
Cost Management
Multi-cloud cost management requires:
- Tagging strategy: Consistent tags across all clouds
- Centralized visibility: Tools like CloudHealth or native cost explorers
- Reserved capacity: Commit to discounts where workloads are stable
- Right-sizing: Regular review of resource utilization
- Automation: Auto-scaling and scheduled scaling
Security Considerations
Identity and Access
- Use a central identity provider (Azure AD, Okta)
- Implement least-privilege access
- Regular access reviews
- Service accounts with minimal permissions
Network Security
- Private connectivity between clouds (ExpressRoute, Direct Connect)
- Encrypt all cross-cloud traffic
- Centralized firewall policies
- Zero-trust networking where possible
Compliance
- Understand data residency requirements
- Maintain compliance certifications per cloud
- Audit logging across all platforms
- Incident response plans for each environment
Lessons Learned
1. Start Simple
Do not go multi-cloud on day one. Master one cloud first, then expand.
2. Invest in Abstractions
The time spent building cloud-agnostic abstractions pays off exponentially.
3. Standardize Operations
Different clouds, same processes. Runbooks, monitoring, and incident response should be consistent.
4. Train Your Team
Certifications matter less than hands-on experience. Give your team time to learn each platform.
5. Document Everything
Why did we choose AWS for this service? Why Azure for that? Future you will thank present you.
Conclusion
Multi-cloud is not a goal in itself. It is a capability that enables flexibility, compliance, and resilience. The key is being intentional about when and why you use multiple clouds, rather than spreading yourself thin across all of them.
Start with business requirements, choose the right platform for each workload, and invest in the tooling and skills that make multi-cloud manageable.