architect

name	architect
description	(forwward) Designs system architecture, selects databases, structures projects, plans APIs, and defines caching strategies with scale-appropriate defaults. Triggers on system design, database selection, project structure, tech stack, API design, caching, or codebase organization.

Architect — System Design

Structure the system before building it. Good architecture makes everything easier. Bad architecture makes everything a rewrite.

Step 0: Understand Constraints

Before designing, answer:

1. Scale — 100 users or 100,000? Determines everything.
2. Team size — Solo founder or 10 engineers? Simpler systems for smaller teams.
3. Budget — Bootstrapped or funded? Managed services vs self-hosted.
4. Timeline — MVP in 2 weeks or v1 in 3 months?
5. Compliance — HIPAA, SOC 2, GDPR? Constrains architecture choices.

Architecture Patterns

Pattern	When to Use	When to Avoid
Monolith	<10 engineers, single product, moving fast	Multiple teams needing independent deploys
Modular monolith	Growing team, want boundaries without infra cost	Need independent scaling per service
Microservices	20+ engineers, distinct domains, independent scaling	Small team, early stage, unclear boundaries
Serverless	Event-driven, spiky traffic, want zero ops	Long-running processes, cost-sensitive at scale
Event-driven	Async workflows, decoupled systems, audit trails	Simple CRUD, real-time requirements

Default: Monolith until it hurts. Premature microservices is the #1 architecture mistake.

Database Selection

Need	Database	Why
General purpose, relational	PostgreSQL	ACID, JSON support, extensions, ecosystem
Key-value, caching	Redis / Valkey	Sub-ms reads, TTL, pub/sub
Document store, flexible schema	MongoDB	Rapid prototyping, nested documents
Full-text search	PostgreSQL FTS or Meilisearch	Postgres built-in is good enough until it isn't
Time-series	TimescaleDB (Postgres extension)	Keep one database engine
Graph relationships	PostgreSQL with recursive CTEs	Don't add Neo4j unless graph is the core product
Vector / embeddings	pgvector (Postgres extension)	Same — keep one database

Rules:

• Start with Postgres. Add specialized databases only when Postgres can't do the job.
• Managed always (Supabase, Neon, RDS). Don't manage your own database.
• One database engine until you have a DBA. Two databases = two problems.

Project Structure

Detect the stack first. Check package.json, go.mod, Gemfile, pyproject.toml, pom.xml, or Cargo.toml before recommending a structure. Apply the principles below to whatever stack the project uses.

Universal rules — apply to every stack:

• Feature code stays together. Don't scatter a feature across 8 directories.
• Services contain business logic. Routes are thin — validate, call service, respond.
• One service per domain, not one file per function.

Example: TypeScript / Next.js

src/
├── app/                    # Next.js app router — pages and layouts
│   ├── (auth)/             # Route groups for auth pages
│   ├── (dashboard)/        # Route groups for app pages
│   └── api/                # API routes
├── lib/                    # Shared utilities, config, constants
│   ├── db/                 # Database client, schema, migrations
│   ├── auth/               # Auth config and helpers
│   └── utils/              # Pure utility functions
├── services/               # Business logic — one file per domain
├── components/             # React components
│   ├── ui/                 # Primitives (button, input, card)
│   └── features/           # Feature-specific composites
└── types/                  # Shared TypeScript types

Example: Python / FastAPI

src/
├── api/
│   ├── v1/                 # Versioned endpoints
│   └── deps.py             # Shared dependencies (auth, db session)
├── core/                   # Config, security, constants
├── models/                 # ORM models
├── schemas/                # Request/response schemas
├── services/               # Business logic — one file per domain
├── repositories/           # Data access layer
└── tests/
    ├── unit/
    └── integration/

Example: Go

cmd/
├── api/                    # Application entrypoint
internal/
├── handler/                # HTTP handlers (thin — validate + call service)
├── service/                # Business logic
├── repository/             # DB access layer
├── model/                  # Domain types
└── config/                 # Config loading
pkg/                        # Shared, importable packages

Example: Ruby on Rails

app/
├── controllers/            # Thin — params, call service, render
├── models/                 # ActiveRecord + domain logic
├── services/               # Complex business logic extracted from models
├── views/                  # Templates or JSON serializers
└── jobs/                   # Background jobs (Sidekiq)
config/
db/
├── migrate/                # Migrations
└── schema.rb

For other stacks (Java/Spring, .NET, Rust, Elixir): apply the same principle — thin routes, services for business logic, repositories for data access, tests alongside the code they test.

API Design

Decision	Default
Protocol	REST for CRUD, tRPC for type-safe full-stack, GraphQL only if multiple clients need different shapes
Versioning	URL path: /api/v1/ — simple, explicit
Auth	Bearer token in Authorization header
Pagination	Cursor-based for feeds, offset for admin tables
Errors	{ error: { code: "NOT_FOUND", message: "User not found" } }
Rate limiting	100 req/min default, lower for auth endpoints

Caching Strategy

Layer	Tool	TTL	Use When
Browser	Cache-Control headers	Varies	Static assets, API responses
CDN	Cloudflare / Vercel Edge	1-60 min	Public pages, images
Application	Redis / in-memory	5-60 min	Expensive queries, session data
Database	Materialized views	Refresh on write	Aggregations, dashboards

Rules:

• Cache reads, not writes. Invalidate on mutation.
• Start with no cache. Add caching when you measure a bottleneck.
• Every cache needs an invalidation strategy. "TTL and hope" works until it doesn't.

Scaling Checklist

Only optimize when you hit the problem:

Users	Likely Bottleneck	Fix
0-1K	Nothing	Don't optimize
1K-10K	Database queries	Add indexes, optimize N+1s
10K-100K	Database connections	Connection pooling (PgBouncer)
100K-1M	Read throughput	Add Redis cache layer
1M+	Write throughput	Read replicas, sharding, queue writes

The best architecture is the simplest one that handles your current scale + 10×.