How I Designed My SaaS Architecture (Simple & Scalable)
When I started building my SaaS, I made one decision early:
Optimize for simplicity first. Scale later.
Most solo developers over-engineer on day one. I’ve done that before—and paid the price.
This time, I designed an architecture that is:
Easy to reason about
Cheap to run
Scalable when I actually need it
This post explains how I designed my SaaS architecture, what I deliberately did not build, and how it can scale without a rewrite.
Core Principles I Followed
- Fewer Moving Parts
Every new service increases:
Cognitive load
Failure points
Maintenance cost
If one tool can solve 80% of the problem reliably, I use it.
- Scale Only What Is Proven
I avoid premature:
Microservices
Queues
Multi-region setups
I design clear boundaries so scaling later is easy—but unnecessary today.
- One Source of Truth
No duplicated logic across systems.
Business rules live in one backend layer.
High-Level Architecture Overview
Frontend
React-based SPA
Static hosting with CDN
Auth handled via backend SDK
Backend
Single API layer
Serverless functions for business logic
REST-based communication
Database
Managed PostgreSQL
Row-level security
SQL-first design (no ORM magic)
Storage
Object storage for uploads
Signed URLs for access control
Why I Avoided Microservices
Microservices make sense when:
You have multiple teams
Independent scaling is required
Failure isolation is critical
As a solo developer:
They slow you down
Debugging becomes painful
Local development is harder
Instead, I built a modular monolith:
Clear folders
Clear domain boundaries
Single deployment unit
You get simplicity now and scalability later.
API Design Strategy
I kept the API intentionally boring.
REST over GraphQL
Explicit endpoints
No hidden magic
Example patterns:
/auth/* → authentication
/projects/* → core resources
/billing/* → payments
Each endpoint:
Validates input
Executes business logic
Returns predictable responses
Database Design Decisions
I chose PostgreSQL for one reason: reliability.
Key choices:
Strict schemas
Foreign keys everywhere
Minimal JSON blobs
Indexes only when measured
This prevents:
Silent data corruption
Hard-to-debug bugs
Performance surprises later
Authentication & Authorization
I separated concerns clearly:
Authentication: Who you are
Authorization: What you can access
Authorization is enforced:
At the database level
Through row-level security
Not just in API code
This gives strong security without extra logic.
Scaling Path (When Needed)
I designed the system so scaling does not require a rewrite.
Step 1: Vertical scaling
Better DB plan
More function memory
Step 2: Caching
Read-heavy endpoints
Session-aware caching
Step 3: Background jobs
Move long tasks to async workers
Add queues only when necessary
No architectural panic. Just incremental upgrades.
What I Deliberately Did NOT Build
No message queues (yet)
No microservices
No event-driven pipelines
No complex CI/CD workflows
These are solutions—not requirements.
Lessons Learned
Simple systems survive longer
Clear boundaries matter more than fancy tools
Most scaling problems never happen
If I hit real scale issues, I’ll be happy—that means the product worked.
Final Thought
Your architecture should serve speed and clarity, not ego.
If you’re building alone or in a small team:
Start simple. Design clean. Scale with evidence.
Question for you:
How would you design this differently—and why?
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