Choosing the Right AWS Architecture for Your Application

AWS offers hundreds of services, which makes choosing the right architecture overwhelming for new projects. After architecting systems that serve millions of users, here's my framework for making these decisions based on your actual requirements—not marketing hype.

Start with Your Constraints

Before diving into services, answer these questions:

• Expected traffic: 100 requests/day vs 10,000 requests/second requires different approaches
• Budget: Serverless vs always-on infrastructure costs differ dramatically
• Team expertise: Don't choose EKS if nobody knows Kubernetes
• Compliance needs: HIPAA, SOC 2, GDPR affect service selection
• Latency requirements: Real-time vs eventual consistency changes everything

Architecture Pattern 1: Serverless First (Recommended for Most)

Best for: Startups, MVPs, and applications with variable traffic

The Stack:

• Compute: AWS Lambda (pay per execution, auto-scales)
• API: API Gateway (managed REST/WebSocket APIs)
• Database: DynamoDB (NoSQL, serverless) or Aurora Serverless (SQL)
• Storage: S3 (object storage, static site hosting)
• Auth: Cognito (user management, OAuth)
• Queue: SQS (simple, reliable message queue)

Why it works: Serverless architectures can handle variable traffic patterns efficiently, scaling automatically from low to peak loads with zero server management and costs that scale with usage.

When to avoid: Consistent high traffic (where Lambda costs exceed EC2), long-running processes (>15 minutes), need for persistent WebSocket connections.

Architecture Pattern 2: Container-Based (For Complex Applications)

Best for: Microservices, complex applications, teams familiar with Docker

The Stack:

• Compute: ECS with Fargate (managed containers, no server management)
• Load Balancer: Application Load Balancer (ALB)
• Database: RDS (PostgreSQL/MySQL) with read replicas
• Cache: ElastiCache Redis (session storage, caching)
• Storage: S3 + CloudFront CDN
• Monitoring: CloudWatch + X-Ray for distributed tracing

Why it works: Container-based architectures enable microservices patterns where each service can be independently scaled and deployed, providing flexibility for complex applications while maintaining reasonable operational costs.

Skip EKS unless: You have dedicated DevOps engineers and genuinely need Kubernetes features. ECS Fargate gives you 80% of the benefits with 20% of the complexity.

Architecture Pattern 3: Traditional VPC (For Regulated Industries)

Best for: Financial services, healthcare, government, high-security requirements

The Stack:

• Compute: EC2 instances in Auto Scaling Groups
• Network: Private VPC with NAT Gateways, private subnets
• Database: RDS in private subnet, encrypted at rest
• Security: WAF (Web Application Firewall), GuardDuty, Security Groups
• Logging: CloudTrail + CloudWatch Logs with retention

Why it works: Traditional VPC architectures provide the security controls and compliance features required for regulated industries. Note that always-on infrastructure means higher baseline costs compared to serverless options.

Database Selection: The Critical Decision

Your database choice impacts everything. Here's my decision tree:

Use DynamoDB when:

• Simple key-value or document queries
• Need guaranteed single-digit millisecond latency
• Variable traffic (serverless billing)
• Global distribution required (DynamoDB Global Tables)

Use RDS (PostgreSQL/MySQL) when:

• Complex queries with joins
• Team familiar with SQL
• Existing codebase uses relational patterns
• Need transactions and ACID guarantees

Use Aurora Serverless when:

• Need SQL but with variable traffic
• Development/staging environments (auto-pause saves money)
• Small to medium databases (scales to ~128GB easily)

Cost Optimization Strategies

AWS bills can spiral quickly. Key strategies:

1. Right-size from day one: Start small, scale up. Over-provisioning wastes thousands monthly.
2. Use Auto Scaling: Don't pay for idle capacity during off-hours.
3. Reserved Instances for predictable loads: 40% discount for 1-year commitment on RDS, EC2.
4. S3 Lifecycle policies: Move old data to cheaper storage classes (S3 Glacier).
5. CloudWatch billing alarms: Set alerts before runaway costs happen.

My Recommended Starting Point

For most new projects, this stack provides a solid foundation:

• Frontend: S3 + CloudFront (static site or SPA)
• API: API Gateway + Lambda (Node.js or Python)
• Database: Aurora Serverless PostgreSQL (SQL familiarity + auto-scaling)
• Auth: Cognito (unless you need very custom auth flows)
• File Storage: S3 with presigned URLs for uploads
• Background Jobs: SQS + Lambda (reliable async processing)

This stack provides cost-effective scaling for early-stage applications and can grow to support large user bases. You can always migrate to containers or EC2 later if specific requirements demand it.

Migration Strategy

Moving to AWS? Do it incrementally:

1. Week 1: Move static assets to S3 + CloudFront
2. Week 2: Database migration (use AWS DMS for minimal downtime)
3. Week 3: API layer (keep old infra running in parallel)
4. Week 4: Gradual traffic cutover using Route 53 weighted routing

Never do a big-bang migration. Incremental moves let you roll back quickly if issues arise.

The Bottom Line

Choose serverless first unless you have a specific reason not to. It's cheaper, scales automatically, and requires minimal maintenance. As you grow and understand your actual usage patterns, you can optimize further.

The best architecture is the one that ships your product quickly while staying within budget. Perfect infrastructure that takes 6 months to build helps nobody.

Need help designing your AWS architecture or migrating existing infrastructure? Let's talk about a pragmatic approach that fits your timeline and budget.