Easy to Scale
Built for growth from day one with scalable architecture and global reach
Modular and Decoupled Architecture
We know tightly coupled systems are fragile systems. Micro-modules or service-based structure are used even if monolith. Grouping is done by features/domains, not tech layers (Domain-Driven Design lite). Frontend/backend are decoupled via API contracts (OpenAPI, GraphQL). Benefits: Easy to isolate, scale, or replace modules without breaking the whole system.
Scalable Tech Stack (Right Tools for Right Work)
We choose scalable ubiquitously runtimes (mainly Node.js) for backend. PostgreSQL, cloud PostgreSQL compatible DBs, Supabase, Firebase are used if needed. Redis are integrated for heavy read traffic or caching. Cloud-native infrastructure and docker are used: AWS / GCP / — with autoscaling support. Obscure tools that can't scale horizontally or lack community support are avoided.
Async + Queue-Based Workflows for Non-Critical Tasks
We know everything doesn't need to happen in real-time. Long-running or background tasks (emails, image processing, billing) are offloaded to queues, etc. Example: After a user signs up, billing/email is processed in the background, not inline. This helps for better performance and scalability.
Data Model and API Design Built to Evolve
We know bad schema = future rework. Versioned APIs (/api/v1/...) are used to prevent breaking clients. UUIDs are favored over incremental IDs (e.g., for merging data across regions). Multi-tenancy is planned for (especially in SaaS): Row-level vs schema-level separation, add tenant_id early.
Monitoring, Observability & Alerts from Day 1
We know you can't scale what you can't see. Tools like Sentry is used for frontend error tracking; Prometheus for backend/infrastructure observability; PostHog, Google analytics for product analytics; Continuing checks for downtime monitoring. Alerts (Slack/email) are set up for crashes, queue backlogs, DB spikes, etc.
Horizontal Scalability and Stateless Services
We know monoliths can scale, but stateless services scale better. Storing user sessions on local memory is avoided — Redis/session stores are used. Servers are made stateless so they can be duplicated easily. Containerization is used: Docker + orchestrators(Cloud providers). This enables autoscaling without sticky sessions or shared memory bottlenecks.
Security and Access Control at Scale
We know more users = more attack surface. RBAC/ABAC patterns (role-based access control) are set up. Rate limiting, input validation, and security headers are applied. Secrets are kept in vaults and rotated.
Scalability Best Practices per Layer
API Layer: Rate limiting, pagination, GraphQL federation (if needed) are implemented. Frontend: Lazy loading, CDN hosting, code splitting are used. Autoscaling are used. Database: Indexes are reindexed, sharding is planned for if needed, and so on.