---

name: tech-stack-recommender description: Recommend technology stacks based on project requirements, team expertise, and constraints. Use when selecting frameworks, languages, databases, and infrastructure for new projects.

Tech Stack Recommender

Provides structured recommendations for technology stack selection based on project requirements, team constraints, and business goals.

When to Use

• Starting a new project and need stack recommendations
• Evaluating technology options for specific use cases
• Comparing frameworks or languages for a project
• Assessing team readiness for a technology choice
• Planning technology migrations

Stack Selection Framework

Decision Inputs

┌───────────────────────────────────────────────────────────────────┐
│                    STACK SELECTION INPUTS                         │
├───────────────────────────────────────────────────────────────────┤
│                                                                   │
│  Project Requirements     Team Factors        Business Constraints│
│  ────────────────────     ────────────        ──────────────────  │
│  • Scale expectations     • Current skills    • Time to market    │
│  • Performance needs      • Learning capacity • Budget            │
│  • Integration points     • Team size         • Hiring market     │
│  • Compliance/Security    • Experience level  • Long-term support │
│                                                                   │
└───────────────────────────────────────────────────────────────────┘
                              │
                              ▼
                    ┌─────────────────┐
                    │ RECOMMENDATION  │
                    │   Framework     │
                    └─────────────────┘

---

Quick Stack Recommendations

By Project Type

Project Type	Frontend	Backend	Database	Why
SaaS MVP	Next.js	Node.js/Express	PostgreSQL	Fast iteration, full-stack JS
E-commerce	Next.js	Node.js or Python	PostgreSQL + Redis	SEO, caching, transactions
Mobile App	React Native	Node.js/Python	PostgreSQL	Cross-platform, shared logic
Real-time App	React	Node.js + WebSocket	PostgreSQL + Redis	Event-driven, low latency
Data Platform	React	Python/FastAPI	PostgreSQL + ClickHouse	Data processing, analytics
Enterprise	React	Java/Spring or .NET	PostgreSQL/Oracle	Stability, enterprise support
ML Product	React	Python/FastAPI	PostgreSQL + Vector DB	ML ecosystem, inference

By Team Profile

Team Profile	Recommended Stack	Avoid
Full-stack JS	Next.js, Node.js, PostgreSQL	Go, Rust (learning curve)
Python Background	FastAPI, React, PostgreSQL	Heavy frontend frameworks
Enterprise Java	Spring Boot, React, PostgreSQL	Bleeding-edge tech
Startup (Speed)	Next.js, Supabase/Firebase	Complex microservices
Scale-Up	React, Go/Node, PostgreSQL	Monolithic frameworks

---

Technology Comparison Tables

Frontend Frameworks

Framework	Best For	Learning Curve	Ecosystem	Hiring
React	Complex UIs, SPAs	Medium	Excellent	Easy
Next.js	Full-stack, SSR, SEO	Medium	Excellent	Easy
Vue.js	Simpler apps, gradual adoption	Easy	Good	Medium
Svelte	Performance-critical	Easy	Growing	Hard
Angular	Enterprise, large teams	Hard	Good	Medium

React vs Vue vs Angular

                Speed to MVP    Long-term Maint    Enterprise Ready
React           ████████░░      ████████░░         █████████░
Vue             █████████░      ███████░░          ██████░░░░
Angular         ██████░░░░      █████████░         ██████████

Backend Frameworks

Framework	Language	Best For	Performance	Ecosystem
Express	Node.js	APIs, real-time	Good	Excellent
Fastify	Node.js	High-performance APIs	Excellent	Good
FastAPI	Python	ML APIs, async	Excellent	Good
Django	Python	Full-featured apps	Good	Excellent
Spring Boot	Java	Enterprise	Good	Excellent
Go (Gin/Echo)	Go	High performance	Excellent	Good
Rails	Ruby	Rapid prototyping	Moderate	Good
NestJS	TypeScript	Structured Node apps	Good	Good

When to Use What

## Node.js (Express/Fastify/NestJS)
✅ Real-time applications (WebSocket)
✅ I/O-heavy workloads
✅ Full-stack JavaScript teams
✅ Microservices
❌ CPU-intensive tasks
❌ Heavy computation

## Python (FastAPI/Django)
✅ ML/Data Science integration
✅ Rapid prototyping
✅ Data processing pipelines
✅ Scientific computing
❌ High-concurrency I/O
❌ Real-time systems

## Go
✅ High-performance services
✅ System programming
✅ Concurrent workloads
✅ Microservices at scale
❌ Rapid prototyping
❌ Complex ORM needs

## Java (Spring Boot)
✅ Enterprise applications
✅ Complex business logic
✅ Transaction-heavy systems
✅ Large teams
❌ Quick MVPs
❌ Small projects

Databases

Database	Type	Best For	Scale	Complexity
PostgreSQL	Relational	General purpose, ACID	High	Medium
MySQL	Relational	Web apps, read-heavy	High	Low
MongoDB	Document	Flexible schemas, JSON	High	Low
Redis	Key-Value	Caching, sessions	Very High	Low
Elasticsearch	Search	Full-text search	High	Medium
ClickHouse	Columnar	Analytics, time-series	Very High	Medium
DynamoDB	Key-Value	Serverless, AWS	Very High	Medium
Cassandra	Wide-column	Write-heavy, distributed	Very High	High

Database Selection Guide

Need ACID transactions?
├── YES → PostgreSQL
│
└── NO → What's your primary use case?
    ├── General purpose → PostgreSQL (still!)
    ├── Document storage → MongoDB
    ├── Caching → Redis
    ├── Search → Elasticsearch
    ├── Analytics → ClickHouse/BigQuery
    ├── Time-series → TimescaleDB/InfluxDB
    └── Key-value at scale → DynamoDB/Cassandra

Infrastructure

Platform	Best For	Complexity	Cost
Vercel	Next.js, frontend	Very Low	$ - $$
Railway	Simple deployments	Low	$ - $$
Render	General apps	Low	$ - $$
AWS	Everything, scale	High	$ - $$$$
GCP	ML/Data, Kubernetes	High	$ - $$$$
Azure	Enterprise, .NET	High	$ - $$$$
DigitalOcean	Simple, affordable	Low	$
Fly.io	Edge, global	Medium	$ - $$

---

Stack Templates

Template 1: Modern SaaS Startup

┌──────────────────────────────────────────────────────────────────┐
│                     MODERN SAAS STACK                            │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          BACKEND            DATABASE                   │
│  ─────────         ───────            ────────                   │
│  Next.js 14        Node.js/Express    PostgreSQL                 │
│  TypeScript        TypeScript         Prisma ORM                 │
│  Tailwind CSS      REST/GraphQL       Redis (cache)              │
│                                                                  │
│  INFRASTRUCTURE    AUTH               PAYMENTS                   │
│  ──────────────    ────               ────────                   │
│  Vercel            Clerk/Auth0        Stripe                     │
│  AWS S3            NextAuth           Stripe Billing             │
│  Cloudflare CDN                                                  │
│                                                                  │
│  MONITORING        CI/CD              ANALYTICS                  │
│  ──────────        ─────              ─────────                  │
│  Sentry            GitHub Actions     PostHog/Amplitude          │
│  Datadog           Vercel Preview     Mixpanel                   │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: B2B SaaS, 0-1M users
Team size: 2-10 engineers
Time to MVP: 4-8 weeks

Template 2: E-Commerce Platform

┌──────────────────────────────────────────────────────────────────┐
│                   E-COMMERCE STACK                               │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          BACKEND            DATABASE                   │
│  ─────────         ───────            ────────                   │
│  Next.js (SSR)     Node.js/Python     PostgreSQL                 │
│  TypeScript        GraphQL/REST       Redis                      │
│  Tailwind/Styled   Medusa/Custom      Elasticsearch              │
│                                                                  │
│  PAYMENTS          SHIPPING           INVENTORY                  │
│  ────────          ────────           ─────────                  │
│  Stripe            ShipStation        Custom/ERP                 │
│  PayPal            EasyPost           Webhook sync               │
│                                                                  │
│  CDN               SEARCH             QUEUE                      │
│  ───               ──────             ─────                      │
│  CloudFront        Algolia/Elastic    SQS/BullMQ                 │
│  Cloudflare        Typesense          Redis                      │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: D2C, Marketplace
Team size: 5-20 engineers
Time to MVP: 8-16 weeks

Template 3: ML-Powered Product

┌──────────────────────────────────────────────────────────────────┐
│                    ML PRODUCT STACK                              │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          API                ML SERVING                 │
│  ─────────         ───                ──────────                 │
│  React/Next.js     FastAPI            TorchServe/Triton          │
│  TypeScript        Python             Docker/K8s                 │
│                    Pydantic           ONNX Runtime               │
│                                                                  │
│  DATABASE          VECTOR DB          FEATURE STORE              │
│  ────────          ─────────          ─────────────              │
│  PostgreSQL        Pinecone           Feast                      │
│  Redis             Weaviate           Redis                      │
│                    pgvector                                      │
│                                                                  │
│  ML OPS            TRAINING           MONITORING                 │
│  ─────             ────────           ──────────                 │
│  MLflow            SageMaker          Weights & Biases           │
│  Airflow           Vertex AI          Prometheus/Grafana         │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: AI products, recommendation systems
Team size: 5-15 engineers + ML team
Time to MVP: 12-24 weeks

Template 4: Real-Time Application

┌──────────────────────────────────────────────────────────────────┐
│                   REAL-TIME STACK                                │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│  FRONTEND          BACKEND            REAL-TIME                  │
│  ─────────         ───────            ─────────                  │
│  React             Node.js            Socket.io                  │
│  TypeScript        Express/Fastify    WebSocket                  │
│                    TypeScript         Redis Pub/Sub              │
│                                                                  │
│  DATABASE          CACHE              MESSAGE QUEUE              │
│  ────────          ─────              ─────────────              │
│  PostgreSQL        Redis              Redis Streams              │
│  Prisma            In-memory          Kafka (scale)              │
│                                                                  │
│  PRESENCE          STATE SYNC         CONFLICT RESOLUTION        │
│  ────────          ──────────         ───────────────────        │
│  Redis             CRDT/OT            Yjs/Automerge              │
│  Custom            LiveBlocks         Custom                     │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

Best for: Chat, collaboration, gaming
Team size: 5-15 engineers
Time to MVP: 8-16 weeks

---

Technology Trade-off Analysis

Language Selection Matrix

Factor	JavaScript/TS	Python	Go	Java	Rust
Learning Curve	Low	Low	Medium	Medium	High
Ecosystem	Excellent	Excellent	Good	Excellent	Growing
Performance	Good	Moderate	Excellent	Good	Excellent
Hiring Pool	Large	Large	Medium	Large	Small
Type Safety	TS: Good	Optional	Excellent	Excellent	Excellent
Memory Safety	GC	GC	GC	GC	Compile-time

Framework Selection Criteria

## Evaluation Checklist

1. **Team Expertise** (Weight: 30%)
   - Current skills alignment?
   - Learning curve acceptable?
   - Training resources available?

2. **Project Requirements** (Weight: 30%)
   - Performance requirements met?
   - Feature set complete?
   - Scalability path clear?

3. **Ecosystem** (Weight: 20%)
   - Package availability?
   - Community size?
   - Third-party integrations?

4. **Long-term Viability** (Weight: 20%)
   - Active maintenance?
   - Corporate backing?
   - Future roadmap?

---

Anti-Patterns to Avoid

Technology Selection Red Flags

Anti-Pattern	Why It's Bad	Better Approach
Resume-Driven	Choosing tech for career, not project	Match to requirements
Hype-Driven	Picking latest without evaluation	Proven over trendy
Comfort-Only	Only familiar tech even when unsuitable	Evaluate objectively
Over-Engineering	Complex stack for simple needs	Start simple
Under-Engineering	Simple tools for complex needs	Plan for growth

Common Mistakes

❌ "Let's use microservices from day one"
   → Start monolith, extract later

❌ "We need Kubernetes for our 3-person startup"
   → Use managed platforms (Vercel, Railway)

❌ "MongoDB because NoSQL is modern"
   → PostgreSQL handles 95% of use cases better

❌ "GraphQL for everything"
   → REST is simpler for most APIs

❌ "Let's build our own auth"
   → Use Auth0, Clerk, or established solutions

---

Migration Considerations

When to Consider Migration

Trigger	Action
Performance bottlenecks	Profile first, then consider
Team expertise mismatch	Train or hire before migrating
End of life/support	Plan 6-12 months ahead
Scale limitations	Validate limits with benchmarks
Security vulnerabilities	Patch if possible, migrate if not

Migration Risk Assessment

LOW RISK:
- Library/package updates
- Minor version upgrades
- Adding new services

MEDIUM RISK:
- Database version upgrades
- Framework major versions
- New deployment platform

HIGH RISK:
- Language/framework rewrites
- Database technology changes
- Monolith to microservices

---

Quick Reference

"I'm building a..."

Project	Recommended Stack
Blog/CMS	Next.js + Headless CMS (Sanity/Contentful)
SaaS Dashboard	Next.js + Node.js + PostgreSQL
Mobile App	React Native + Node.js + PostgreSQL
E-commerce	Next.js + Medusa/Custom + PostgreSQL
Real-time Chat	React + Node.js + Socket.io + Redis
Data Dashboard	React + Python/FastAPI + PostgreSQL
ML Product	React + Python/FastAPI + PostgreSQL + Vector DB
API Service	Node.js or Python + PostgreSQL

Stack Complexity Levels

Complexity	Description	Example Stack
Minimal	Single deployment, managed services	Vercel + Supabase
Simple	Separate frontend/backend	Vercel + Railway + PostgreSQL
Standard	Multiple services, caching	AWS ECS + RDS + Redis
Complex	Microservices, event-driven	K8s + Multiple DBs + Kafka

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References

• Framework Comparison - Detailed feature comparisons
• Migration Playbooks - Step-by-step migration guides