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
Copy ┌───────────────────────────────────────────────────────────────────┐
│ 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
Copy 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
markdown Copy ## 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
Copy 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
Copy ┌──────────────────────────────────────────────────────────────────┐
│ 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
Copy ┌──────────────────────────────────────────────────────────────────┐
│ 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
Copy ┌──────────────────────────────────────────────────────────────────┐
│ 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
Copy ┌──────────────────────────────────────────────────────────────────┐
│ 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
markdown Copy ## 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
markdown Copy ❌ "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
Copy 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
References