playbook/outfitter-agents/plugins/outfitter/skills/architecture/references/technology-selection.md

Technology Selection

Database Selection

Decision factors:

1. Data model fit

   • Relational (structured, ACID, complex queries) → PostgreSQL, MySQL
   • Document (flexible schema, nested data) → MongoDB, DynamoDB
   • Graph (relationship-heavy) → Neo4j, DGraph
   • Time-series (metrics, events) → TimescaleDB, InfluxDB
   • Key-value (simple lookups, cache) → Redis, DynamoDB

2. Consistency requirements

   • Strong consistency → PostgreSQL, CockroachDB
   • Eventual consistency acceptable → DynamoDB, Cassandra
   • Hybrid needs → MongoDB, Cosmos DB

3. Scale characteristics

   • Read-heavy → read replicas, caching
   • Write-heavy → sharding, write-optimized DB
   • Both → consider CQRS pattern

4. Operational complexity

   • Managed service available? Use it unless special needs
   • Self-hosted required? Factor operational overhead
   • Multi-region? Consider distributed databases

Decision matrix:

ACID + complex queries + proven? → PostgreSQL
Flexibility + horizontal scaling + managed? → DynamoDB
Document model + rich queries + open source? → MongoDB
High write throughput + wide column? → Cassandra
Caching + pub/sub + simple data? → Redis

Framework Selection

Backend (TypeScript/JavaScript)

Framework	Best for
Hono	New projects, serverless, edge
Express	Teams with Express experience
Fastify	Raw speed matters
NestJS	Large teams, complex domains

Backend (Rust)

Framework	Best for
Axum	New projects, type-safe routing
Actix-web	Raw performance critical
Rocket	Rapid development

Decision criteria:

• Team expertise and learning curve
• Performance requirements (most apps don't need Rust speed)
• Ecosystem and library availability
• Type safety and developer experience
• Deployment target (serverless, containers, bare metal)

Frontend

Stack	Best for
React + TanStack Router	Complex state, large ecosystem
Solid	Performance-critical UIs
Svelte	Small teams, simple apps
Next.js	SSR/SSG needs, full-stack React

Infrastructure

Serverless (Vercel, Cloudflare Workers, AWS Lambda)

• Pros: Zero ops, auto-scaling, pay-per-use
• Cons: Cold starts, vendor lock-in, harder debugging
• Best for: Low-traffic apps, edge functions, prototypes

Container orchestration (Kubernetes, ECS)

• Pros: Portability, fine control, proven at scale
• Cons: Operational complexity, learning curve
• Best for: Medium-large apps, multi-service systems

Platform-as-a-Service (Heroku, Render, Railway)

• Pros: Simple deploys, managed infrastructure
• Cons: Higher cost, less control, scaling limits
• Best for: Startups, MVPs, small teams

Bare metal / VMs

• Pros: Full control, cost-effective at scale
• Cons: High operational burden
• Best for: Special requirements, very large scale