System Architecture
Design scalable systems, microservices architecture, cloud infrastructure, and database design.
About this service
We help organizations design and implement robust, scalable architectures that grow with their business. From monolithic to microservices transitions, we guide you through the complexities of modern system design.
Ecosystem Overview
A high-level view of how four public-facing sites share a common application backbone. Every external request lands on the reverse proxy, which terminates TLS and routes to the application server; the application server is the only component that talks to data, identity, payment, email, and automation services.
Architecture Decision Records (ADRs)
Every significant architecture decision should be documented with context, alternatives considered, and rationale. ADRs create an institutional memory that survives team turnover — new engineers can understand why the system is built the way it is, not just how.
Why ADRs Matter:
- Prevent revisiting decisions that were already carefully evaluated
- Provide onboarding context for new team members
- Create accountability and traceability for architecture evolution
- Enable informed reversal when requirements change
ADR Template (Markdown)
# ADR-0042: Use PostgreSQL as Primary Database ## Status Accepted (2026-01-15) ## Context We need a primary database for our booking platform. Expected load: 10K concurrent users, 500K daily transactions. Must support ACID transactions for payment processing. ## Decision Use PostgreSQL 16 with read replicas for scaling. ## Alternatives Considered - **MongoDB**: Better for flexible schemas, but we need strong consistency for financial transactions. - **DynamoDB**: Lower ops burden, but vendor lock-in and complex query patterns for our relational data model. - **CockroachDB**: Excellent distributed SQL, but higher cost and operational complexity for our current scale. ## Consequences + Mature ecosystem, excellent tooling (pgAdmin, Prisma) + Strong ACID guarantees for payment flows + Read replicas handle read-heavy dashboard queries - Vertical scaling limits (mitigated by read replicas) - Schema migrations require careful coordination
Architecture Patterns
Choosing the right architecture pattern is the most consequential technical decision you will make. Each pattern optimizes for different quality attributes and comes with real trade-offs.
| Pattern | Best For | Trade-offs | Scale |
|---|---|---|---|
| Monolithic | MVPs, small teams, well-understood domains | Simple deployment but tight coupling; all-or-nothing scaling | Vertical |
| Microservices | Large teams, independent deployment cycles, polyglot stacks | Independent scaling but distributed complexity, network overhead | Horizontal per-service |
| Event-Driven | Real-time systems, loose coupling, async workflows | Excellent decoupling but harder debugging, eventual consistency | Horizontal + async |
| CQRS | Read-heavy systems, complex domains, reporting workloads | Optimized read/write but increased complexity, data synchronization | Independent read/write |
| Serverless | Bursty workloads, event handlers, cost-sensitive operations | Zero idle cost but cold starts, vendor lock-in, execution limits | Auto (per-invocation) |
| Hexagonal | Domain-driven design, high testability requirements | Clean separation of concerns but more boilerplate, steeper learning curve | Depends on infra |
Start monolithic, extract services when you have a proven reason to.
Premature decomposition into microservices is one of the most expensive mistakes a team can make. Wait until you have clear bounded contexts and independent deployment needs.
System Design Process
Our seven-phase architecture engagement takes you from initial discovery to a governed, evolving system design.
Discovery Workshop
Stakeholder interviews, business driver identification, constraint mapping
Current State Assessment
Existing system inventory, dependency mapping, bottleneck analysis
Quality Attributes
Prioritize the -ilities: which matter most for your specific context
Architecture Governance
Fitness functions, architectural decision log, review cadence
Quality Attributes — The “-ilities”
Architecture is ultimately about satisfying quality attribute requirements. Functional requirements tell you what the system does; quality attributes tell you how well it does it.
Handle growing load by adding resources horizontally or vertically
Consistently perform the intended function without failure
Remain operational and accessible when needed (99.9%+ uptime)
Easily modify, extend, and debug without introducing regressions
Protect against unauthorized access, data breaches, and attacks
Respond within acceptable time under expected and peak loads
Understand internal state from external outputs: logs, metrics, traces
Verify behavior through automated tests at every level of the stack
Cloud Architecture Comparison
Each cloud provider has strengths in different areas. We help you choose based on your specific workload requirements, team expertise, and business constraints — not marketing material.
| Service Category | AWS | Azure | GCP |
|---|---|---|---|
| Compute | EC2, ECS, Fargate | Virtual Machines, ACI | Compute Engine, Cloud Run |
| Storage | S3, EBS, EFS | Blob Storage, Managed Disks | Cloud Storage, Persistent Disk |
| Database | RDS, Aurora, DynamoDB | SQL Database, Cosmos DB | Cloud SQL, Spanner, Firestore |
| Serverless | Lambda | Azure Functions | Cloud Functions |
| Container Orchestration | EKS, ECS | AKS | GKE |
| CDN | CloudFront | Azure CDN / Front Door | Cloud CDN |
Infrastructure as Code
Infrastructure should be versioned, reviewed, and tested just like application code. We help teams adopt IaC practices that eliminate configuration drift and enable repeatable, auditable deployments.
Declarative (Terraform/HCL)
- Cloud-agnostic state management
- Plan before apply workflow
- Large module ecosystem
- State locking for team collaboration
Imperative (AWS CDK/Pulumi)
- Full programming language power
- Type safety and IDE support
- Reusable constructs and abstractions
- Unit testable infrastructure
Example: AWS CDK (TypeScript) — VPC with Public Subnet
import * as ec2 from 'aws-cdk-lib/aws-ec2';
import { Stack, StackProps } from 'aws-cdk-lib';
export class NetworkStack extends Stack {
public readonly vpc: ec2.Vpc;
constructor(scope: Construct, id: string, props?: StackProps) {
super(scope, id, props);
this.vpc = new ec2.Vpc(this, 'AppVpc', {
maxAzs: 3,
cidr: '10.50.0.0/16',
subnetConfiguration: [
{
name: 'Public',
subnetType: ec2.SubnetType.PUBLIC,
cidrMask: 24,
},
{
name: 'Private',
subnetType: ec2.SubnetType.PRIVATE_WITH_EGRESS,
cidrMask: 24,
},
],
});
}
}Example: Terraform — Equivalent VPC Configuration
resource "aws_vpc" "app_vpc" {
cidr_block = "10.50.0.0/16"
enable_dns_support = true
enable_dns_hostnames = true
tags = {
Name = "app-vpc"
Environment = "production"
ManagedBy = "terraform"
}
}
resource "aws_subnet" "public" {
count = 3
vpc_id = aws_vpc.app_vpc.id
cidr_block = cidrsubnet("10.50.0.0/16", 8, count.index)
availability_zone = data.aws_availability_zones.available.names[count.index]
map_public_ip_on_launch = true
}The Key Insight
“Architecture is about trade-offs, not best practices — every decision should be justified by your specific quality attribute requirements.”
Ready to design a system architecture that scales with your business? Contact admin@innosaid.com to schedule an architecture consultation.
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