Cloud platforms changed how we build applications. Instead of managing servers, we can focus on building features. But cloud platforms are complex—understanding architectural patterns is essential for building systems that scale and cost-effectively.
Serverless Architecture: Functions as a Service
Serverless computing eliminates server management. AWS Lambda, Azure Functions, and Google Cloud Functions execute code in response to events without provisioning servers.
I've built serverless applications that handle variable workloads efficiently. During peak traffic, functions scale automatically. During low traffic, you pay only for execution time.
Serverless works well for:
- Event-driven processing (file uploads, database changes, API requests)
- Microservices with variable load
- Background jobs and scheduled tasks
- API endpoints with sporadic traffic
For a file processing service, serverless reduced costs by 70% compared to always-on servers. Functions only ran when files were uploaded, scaling automatically during peak times.
Serverless challenges:
- Cold starts (first invocation after idle period)
- Execution time limits (15 minutes for Lambda)
- Vendor lock-in (platform-specific APIs)
- Debugging complexity (distributed execution)
Microservices on Cloud
Cloud platforms provide services that make microservices easier:
AWS
- ECS/EKS for container orchestration
- API Gateway for API management
- Elastic Load Balancing for traffic distribution
- CloudWatch for monitoring
Azure
- Azure Kubernetes Service (AKS) for containers
- API Management for APIs
- Application Gateway for load balancing
- Application Insights for monitoring
GCP
- Google Kubernetes Engine (GKE) for containers
- Cloud Endpoints for API management
- Cloud Load Balancing
- Cloud Monitoring
I've deployed microservices on all three platforms. Each has strengths, but the patterns are similar: containers, service discovery, load balancing, and monitoring.
Container Orchestration: Kubernetes
Kubernetes manages containerized applications. It handles deployment, scaling, and management automatically.
I use Kubernetes for applications that need:
- High availability (automatic restarts, health checks)
- Auto-scaling (scale based on CPU, memory, or custom metrics)
- Rolling updates (zero-downtime deployments)
- Service discovery (automatic DNS and load balancing)
Kubernetes has a learning curve, but managed services (EKS, AKS, GKE) reduce operational overhead. I've deployed applications that scale from 2 to 20 pods automatically based on load.
Auto-Scaling Strategies
Cloud platforms provide auto-scaling, but configuring it correctly requires understanding your application's behavior.
I configure auto-scaling based on:
- CPU utilization: Simple but may not reflect actual load
- Memory utilization: Important for memory-intensive applications
- Request rate: Better for web applications
- Custom metrics: Most accurate but requires instrumentation
For a web application, I use request rate for auto-scaling. When requests per second exceed a threshold, new instances launch. When traffic decreases, instances terminate.
Important: Configure scaling policies carefully. Aggressive scaling can cause cost overruns. Conservative scaling can cause performance problems.
Multi-Cloud Approaches
Some organizations use multiple cloud platforms for redundancy or to avoid vendor lock-in. I've designed systems that run on AWS and Azure simultaneously.
Multi-cloud challenges:
- Increased complexity (different APIs, tools, processes)
- Higher costs (managing multiple platforms)
- Data synchronization (keeping data consistent across clouds)
- Network latency (cross-cloud communication)
Multi-cloud makes sense when:
- Regulatory requirements mandate redundancy
- Different clouds excel at different workloads
- Vendor lock-in is a concern
For most applications, single-cloud is simpler and more cost-effective.
Cost Optimization
Cloud costs can spiral without proper management. I optimize costs by:
- Right-sizing resources: Use instances that match actual needs
- Reserved instances: Commit to 1-3 year terms for predictable workloads
- Spot instances: Use for fault-tolerant workloads
- Auto-scaling: Scale down during low-traffic periods
- Monitoring costs: Set up alerts for unexpected spending
I've reduced cloud costs by 40% through right-sizing and reserved instances. Monitoring revealed over-provisioned resources that could be downsized.
Security in the Cloud
Cloud security is a shared responsibility. Platforms secure infrastructure; you secure applications and data.
I implement:
- Identity and Access Management (IAM) with least privilege
- Encryption at rest and in transit
- Network security groups and firewalls
- Secrets management (AWS Secrets Manager, Azure Key Vault, GCP Secret Manager)
- Regular security audits and vulnerability scanning
I've seen security breaches caused by misconfigured IAM policies or exposed secrets. Cloud platforms provide tools, but proper configuration is essential.
Disaster Recovery
Cloud platforms enable disaster recovery strategies:
- Backup and restore: Regular backups with automated restore testing
- Multi-region deployment: Deploy to multiple regions for redundancy
- Replication: Replicate data across regions automatically
- Failover: Automatic failover to backup regions
I've designed systems with RPO (Recovery Point Objective) of minutes and RTO (Recovery Time Objective) of seconds. Cloud platforms make this achievable.
What I've Learned
After building cloud-native applications:
- Start simple. Use managed services before building custom solutions.
- Monitor everything. Cloud platforms provide extensive monitoring—use it.
- Optimize costs continuously. Cloud costs can grow unexpectedly.
- Design for failure. Cloud resources fail—design systems that handle failures gracefully.
- Understand platform differences. Each cloud platform has strengths—choose based on needs.
Cloud architecture is about using platform capabilities effectively. Understand the patterns, use managed services, monitor everything, and optimize continuously.