67f3922e4f
Phase 1 — Agent & Command Foundation: - Rewrite cs-project-manager agent (55→515 lines, 4 workflows, 6 skill integrations) - Expand cs-product-manager agent (408→684 lines, orchestrates all 8 product skills) - Add 7 slash commands: /rice, /okr, /persona, /user-story, /sprint-health, /project-health, /retro Phase 2 — Script Gap Closure (2,779 lines): - jira-expert: jql_query_builder.py (22 patterns), workflow_validator.py - confluence-expert: space_structure_generator.py, content_audit_analyzer.py - atlassian-admin: permission_audit_tool.py - atlassian-templates: template_scaffolder.py (Confluence XHTML generation) Phase 3 — Reference & Asset Enrichment: - 9 product references (competitive-teardown, landing-page-generator, saas-scaffolder) - 6 PM references (confluence-expert, atlassian-admin, atlassian-templates) - 7 product assets (templates for PRD, RICE, sprint, stories, OKR, research, design system) - 1 PM asset (permission_scheme_template.json) Phase 4 — New Agents: - cs-agile-product-owner, cs-product-strategist, cs-ux-researcher Phase 5 — Integration & Polish: - Related Skills cross-references in 8 SKILL.md files - Updated product-team/CLAUDE.md (5→8 skills, 6→9 tools, 4 agents, 5 commands) - Updated project-management/CLAUDE.md (0→12 scripts, 3 commands) - Regenerated docs site (177 pages), updated homepage and getting-started Quality audit: 31 files reviewed, 29 PASS, 2 fixed (copy-frameworks.md, governance-framework.md) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
279 lines
8.8 KiB
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279 lines
8.8 KiB
Markdown
# Technology Stack Comparison
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## Overview
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Choosing the right technology stack is one of the most impactful early decisions for a SaaS product. This comparison covers the most popular options across frontend, backend, database, and caching layers, with decision criteria for each.
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## Frontend Frameworks
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### Next.js (React)
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**Strengths:**
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- Largest ecosystem and community
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- Excellent developer tooling and documentation
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- Server-side rendering (SSR) and static generation (SSG) built in
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- Vercel deployment makes hosting trivial
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- App Router with React Server Components for optimal performance
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- Rich component library ecosystem (shadcn/ui, Radix, Chakra)
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**Weaknesses:**
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- React learning curve (hooks, state management, rendering model)
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- Bundle size can grow without discipline
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- Vercel lock-in concerns for advanced features
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- Frequent major version changes
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**Best for:** Most SaaS products, teams with React experience, SEO-important pages
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### Remix (React)
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**Strengths:**
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- Web standards focused (forms, HTTP, progressive enhancement)
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- Excellent data loading patterns (loaders/actions)
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- Built-in error boundaries and optimistic UI
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- Works without JavaScript enabled
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- Strong TypeScript support
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- Deployable anywhere (not tied to specific platform)
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**Weaknesses:**
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- Smaller ecosystem than Next.js
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- Fewer deployment guides and hosting templates
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- Less community content and tutorials
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- Now merged into React Router v7 (transition period)
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**Best for:** Data-heavy applications, teams valuing web standards, progressive enhancement needs
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### SvelteKit (Svelte)
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**Strengths:**
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- Smallest bundle sizes (compiler-based, no virtual DOM)
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- Simplest learning curve among frameworks
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- Built-in state management (reactive declarations)
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- Excellent performance out of the box
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- Growing ecosystem and community
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- First-class TypeScript support
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**Weaknesses:**
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- Smaller ecosystem and component library selection
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- Fewer developers in hiring pool
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- Less enterprise adoption (harder to find case studies)
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- Fewer third-party integrations
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**Best for:** Performance-critical applications, small teams wanting simplicity, developer experience priority
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### Frontend Decision Criteria
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| Criterion | Next.js | Remix | SvelteKit |
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|-----------|---------|-------|-----------|
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| Ecosystem Size | Large | Medium | Growing |
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| Learning Curve | Medium | Medium | Low |
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| Performance | Good | Good | Excellent |
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| SSR/SSG | Excellent | Good | Good |
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| Hiring Pool | Large | Small | Small |
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| Bundle Size | Medium | Small | Smallest |
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| TypeScript | Excellent | Excellent | Excellent |
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| Deployment Flexibility | Medium | High | High |
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## Backend Frameworks
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### Node.js (Express / Fastify / NestJS)
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**Strengths:**
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- Same language as frontend (JavaScript/TypeScript full-stack)
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- Massive npm ecosystem
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- NestJS provides enterprise patterns (DI, modules, decorators)
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- Excellent for I/O-heavy workloads
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- Large community and hiring pool
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- Great for real-time features (WebSockets)
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**Weaknesses:**
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- Single-threaded (CPU-intensive tasks require workers)
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- Callback/async complexity
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- npm dependency security concerns
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- Less suited for computational workloads
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**Best for:** Full-stack TypeScript teams, real-time applications, API-heavy products
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### Python (FastAPI / Django)
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**Strengths:**
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- FastAPI: Modern, fast, automatic OpenAPI docs, async support
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- Django: Batteries included (admin, ORM, auth, migrations)
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- Excellent for data processing and ML integration
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- Clean, readable syntax
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- Strong ecosystem for analytics and data work
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- Large hiring pool across web and data roles
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**Weaknesses:**
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- Slower runtime than Go/Rust (mitigated by async in FastAPI)
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- GIL limits true parallelism (multiprocessing required)
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- Django can feel heavyweight for microservices
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- Deployment can be more complex (WSGI/ASGI setup)
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**Best for:** Data-heavy products, ML integration, rapid prototyping, admin-heavy applications
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### Go (Gin / Echo / Fiber)
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**Strengths:**
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- Excellent performance (compiled, concurrent by design)
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- Low memory footprint
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- Simple deployment (single binary, no runtime)
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- Built-in concurrency (goroutines, channels)
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- Strong standard library
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- Fast compilation
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**Weaknesses:**
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- Smaller web ecosystem than Node.js or Python
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- More verbose for CRUD operations
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- Error handling verbosity
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- Fewer ORM options (GORM is the main choice)
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- Steeper learning curve for teams from dynamic languages
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**Best for:** High-throughput APIs, microservices, infrastructure tooling, performance-critical backends
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### Backend Decision Criteria
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| Criterion | Node.js | Python | Go |
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|-----------|---------|--------|-----|
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| Performance | Good | Moderate | Excellent |
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| Developer Productivity | High | High | Medium |
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| Ecosystem | Largest | Large | Medium |
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| Hiring Pool | Large | Large | Medium |
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| Full-Stack Synergy | Excellent | None | None |
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| Data/ML Integration | Medium | Excellent | Low |
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| Concurrency | Event Loop | Async/Threads | Goroutines |
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| Deployment Simplicity | Medium | Medium | High |
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## Database
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### PostgreSQL
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**Strengths:**
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- ACID compliant with excellent reliability
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- Rich feature set (JSON, full-text search, GIS, arrays)
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- Extensible (custom types, functions, extensions like PostGIS, pgvector)
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- Strong community and tooling
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- Excellent for complex queries and analytics
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- Free and open source with managed options (AWS RDS, Supabase, Neon)
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**Weaknesses:**
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- Horizontal scaling requires effort (Citus, partitioning)
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- More complex initial setup than MySQL
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- VACUUM maintenance at high write volumes
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- Slightly slower for simple read-heavy workloads vs MySQL
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**Best for:** Most SaaS applications (recommended default), complex data models, JSON workloads
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### MySQL
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**Strengths:**
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- Proven at massive scale (Meta, Uber, Shopify)
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- Simpler replication setup
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- Faster for simple read-heavy workloads
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- PlanetScale offers serverless MySQL with branching
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- Wide hosting support
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**Weaknesses:**
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- Fewer advanced features than PostgreSQL
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- Weaker JSON support
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- Less extensible
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- InnoDB limitations for certain workloads
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**Best for:** Read-heavy applications, teams with MySQL expertise, PlanetScale users
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### Database Decision Criteria
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| Criterion | PostgreSQL | MySQL |
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|-----------|-----------|-------|
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| Feature Richness | Excellent | Good |
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| JSON Support | Excellent | Moderate |
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| Replication | Good | Good |
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| Horizontal Scale | Moderate | Good (PlanetScale) |
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| Community | Excellent | Excellent |
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| Managed Options | Many | Many |
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| Learning Curve | Medium | Low |
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| Default Choice | Yes | Situational |
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## Caching Layer
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### Redis
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**Strengths:**
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- Rich data structures (strings, hashes, lists, sets, sorted sets, streams)
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- Pub/Sub for real-time messaging
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- Lua scripting for atomic operations
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- Persistence options (RDB, AOF)
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- Cluster mode for horizontal scaling
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- Used for caching, sessions, queues, rate limiting, leaderboards
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**Weaknesses:**
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- Memory-bound (dataset must fit in RAM)
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- Single-threaded command processing
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- Licensing changes (Redis 7.4+ source-available)
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- Cluster mode adds complexity
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**Best for:** Most SaaS applications (recommended default), session management, rate limiting, queues
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### Memcached
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**Strengths:**
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- Simplest possible key-value cache
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- Multi-threaded (better CPU utilization for simple operations)
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- Lower memory overhead per key
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- Predictable performance characteristics
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- Battle-tested at scale
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**Weaknesses:**
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- No data structures (strings only)
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- No persistence
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- No pub/sub or scripting
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- No built-in clustering (client-side sharding)
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- Limited eviction policies
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**Best for:** Pure caching use cases, simple key-value lookups, memory efficiency priority
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### Cache Decision Criteria
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| Criterion | Redis | Memcached |
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|-----------|-------|-----------|
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| Data Structures | Rich | Strings Only |
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| Persistence | Yes | No |
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| Pub/Sub | Yes | No |
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| Multi-Threading | No (I/O threads in v6) | Yes |
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| Use Cases | Many | Caching Only |
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| Memory Efficiency | Good | Better |
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| Default Choice | Yes | Rarely |
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## Recommended Stacks by Product Type
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### B2B SaaS (Most Common)
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- **Frontend:** Next.js + TypeScript + shadcn/ui
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- **Backend:** Node.js (NestJS) or Python (FastAPI)
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- **Database:** PostgreSQL
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- **Cache:** Redis
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- **Auth:** Auth0 or Clerk
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- **Payments:** Stripe
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### Developer Tool / API Product
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- **Frontend:** Next.js or SvelteKit
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- **Backend:** Go (Gin) or Node.js (Fastify)
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- **Database:** PostgreSQL
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- **Cache:** Redis
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- **Auth:** Custom JWT + API Keys
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- **Docs:** Mintlify or ReadMe
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### Data-Heavy / Analytics Product
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- **Frontend:** Next.js
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- **Backend:** Python (FastAPI)
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- **Database:** PostgreSQL + ClickHouse (analytics)
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- **Cache:** Redis
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- **Processing:** Celery or Temporal
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- **Visualization:** Custom or embedded (Metabase)
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### Real-Time / Collaboration Product
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- **Frontend:** Next.js or SvelteKit
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- **Backend:** Node.js (Fastify) + WebSockets
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- **Database:** PostgreSQL + Redis (pub/sub)
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- **Cache:** Redis
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- **Real-Time:** Socket.io or Liveblocks
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- **CRDT:** Yjs or Automerge (for collaborative editing)
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