At Excelsior, we've architected and deployed hundreds of cloud applications across AWS, Azure, and Google Cloud. Here's what we've learned about building systems that scale reliably.

Foundation: The 12-Factor App

Every cloud application we build follows the 12-Factor App methodology:

1. Codebase - One codebase tracked in version control
2. Dependencies - Explicitly declare and isolate dependencies
3. Config - Store config in the environment
4. Backing Services - Treat backing services as attached resources
5. Build, Release, Run - Strictly separate build and run stages
6. Processes - Execute the app as stateless processes
7. Port Binding - Export services via port binding
8. Concurrency - Scale out via the process model
9. Disposability - Maximize robustness with fast startup and graceful shutdown
10. Dev/Prod Parity - Keep development, staging, and production as similar as possible
11. Logs - Treat logs as event streams
12. Admin Processes - Run admin/management tasks as one-off processes

Architecture Patterns We Use

1. Microservices Architecture

When to Use: Complex applications with multiple teams

Benefits:

• Independent scaling
• Technology flexibility
• Faster deployment cycles
• Fault isolation

Challenges:

• Increased complexity
• Network latency
• Data consistency
• Monitoring overhead

2. Serverless Architecture

When to Use: Event-driven applications, variable traffic

Benefits:

• No server management
• Pay-per-use pricing
• Automatic scaling
• Reduced operational overhead

Technologies:

• AWS Lambda / Step Functions
• Azure Functions
• Google Cloud Functions
• Cloudflare Workers

3. Event-Driven Architecture

When to Use: Real-time processing, decoupled systems

Components:

• Message brokers (Kafka, RabbitMQ, AWS SQS)
• Event streams (Kinesis, Event Hubs)
• Pub/Sub systems

Advantages:

• Loose coupling
• Easy to scale
• Resilient to failures

Infrastructure as Code

We use IaC for all cloud deployments:

Terraform (Our Primary Choice)

resource "aws_instance" "app_server" {
  ami           = var.ami_id
  instance_type = "t3.medium"

  tags = {
    Name        = "AppServer"
    Environment = var.environment
  }
}

Why Terraform:

• Multi-cloud support
• Large ecosystem
• State management
• Plan before apply

Alternative Tools

• AWS CDK - For AWS-only deployments
• Pulumi - For teams preferring TypeScript/Python
• CloudFormation - AWS native option

CI/CD Pipeline Design

Our standard pipeline:

1. Code Commit → GitHub/GitLab
2. Automated Tests → Unit, Integration, E2E
3. Security Scanning → SAST, Dependency checks
4. Build & Package → Docker images
5. Deploy to Staging → Automated deployment
6. Smoke Tests → Basic functionality checks
7. Deploy to Production → Blue-green or canary
8. Monitor → Logs, metrics, alerts

Tools We Use

• CI/CD: GitHub Actions, GitLab CI, Jenkins
• Container Registry: ECR, Harbor, Docker Hub
• Orchestration: Kubernetes, ECS, Cloud Run

Security Best Practices

1. Identity and Access Management

• Use least-privilege principle
• Implement role-based access control
• Enable MFA everywhere
• Rotate credentials regularly

2. Network Security

• Use VPCs and private subnets
• Implement network segmentation
• Enable DDoS protection
• Use Web Application Firewalls

3. Data Protection

• Encrypt data at rest and in transit
• Regular backups with testing
• Implement data retention policies
• Use secrets management (AWS Secrets Manager, HashiCorp Vault)

4. Compliance

• SOC 2 compliance
• GDPR requirements
• HIPAA for healthcare
• PCI DSS for payment data

Monitoring and Observability

The Three Pillars

1. Logs

• Centralized logging (ELK, Splunk, Datadog)
• Structured logging format
• Log retention policies

2. Metrics

• Application metrics (Prometheus, CloudWatch)
• Infrastructure metrics (CPU, memory, disk)
• Business metrics (transactions, users)

3. Traces

• Distributed tracing (Jaeger, X-Ray)
• Request correlation IDs
• Performance bottleneck identification

Key Metrics to Monitor

• Availability: Uptime percentage
• Latency: Response times (p50, p95, p99)
• Throughput: Requests per second
• Error Rate: 4xx and 5xx errors
• Saturation: Resource utilization

Cost Optimization

Strategies We Implement

1. Right-Sizing
   • Monitor actual usage
   • Adjust instance sizes accordingly
   • Use auto-scaling effectively
2. Reserved Instances
   • 1-3 year commitments for stable workloads
   • 40-60% cost savings
3. Spot Instances
   • Use for fault-tolerant workloads
   • 70-90% cost savings
4. Storage Optimization
   • Lifecycle policies for old data
   • Use appropriate storage tiers
   • Compress and deduplicate
5. Serverless When Appropriate
   • No cost when idle
   • Pay only for actual usage

Database Architecture

Relational Databases

• AWS RDS (PostgreSQL, MySQL)
• Azure SQL Database
• Google Cloud SQL

Use Cases: Transactional data, complex queries

NoSQL Databases

• DynamoDB - Key-value, high scale
• MongoDB - Document store
• Cassandra - Wide-column, high availability

Use Cases: High-volume reads/writes, flexible schema

Caching Layers

• Redis - In-memory cache
• Memcached - Simple caching
• CDN - Static content delivery

Benefits: 10-100x performance improvement

Disaster Recovery

Backup Strategy

• RPO (Recovery Point Objective): How much data loss is acceptable?
• RTO (Recovery Time Objective): How quickly must systems recover?

DR Patterns

1. Backup and Restore (Slowest, Cheapest)

• Regular backups to S3/Azure Blob
• Restore when needed
• RTO: Hours to days

2. Pilot Light (Medium Speed/Cost)

• Minimal services always running
• Scale up during disaster
• RTO: Minutes to hours

3. Warm Standby (Fast, Expensive)

• Scaled-down version always running
• Quick scale-up
• RTO: Minutes

4. Multi-Region Active-Active (Fastest, Most Expensive)

• Full deployment in multiple regions
• Instant failover
• RTO: Seconds

Real-World Example: E-Commerce Platform

Let's look at a production architecture we built:

Requirements

• 1M+ daily active users
• 99.99% uptime SLA
• Global presence
• PCI DSS compliant

Solution

• Frontend: Next.js on Vercel Edge
• API Gateway: AWS API Gateway
• Application: Kubernetes on EKS
• Database: Aurora PostgreSQL (Multi-AZ)
• Cache: Redis Cluster
• Queue: SQS + Lambda for async processing
• CDN: CloudFront
• Search: Elasticsearch
• Monitoring: Datadog
• Cost: ~$15,000/month at scale

Results

• 99.997% actual uptime
• Less than 100ms API response time (p95)
• Handled Black Friday traffic (10x normal)
• Successfully passed PCI audit

Getting Started

Building cloud architecture right from the start saves money and headaches later. At Excelsior, we:

1. Assess your current infrastructure and requirements
2. Design a scalable, secure architecture
3. Implement using Infrastructure as Code
4. Monitor and optimize continuously
5. Support your team with ongoing maintenance

Our cloud architects have certifications across:

• AWS Solutions Architect Professional
• Azure Solutions Architect Expert
• Google Cloud Professional Architect
• Kubernetes Certified Administrator

Ready to build or optimize your cloud infrastructure? Let's talk.

Questions about cloud architecture? Our team is here to help. Contact us today.