Technology Stack Selection

How to choose AI-agent-friendly tech stacks that work well together

Intermediate ⏱️ 25 minutes

Technology Stack Selection

The key to successful AI coding agent development is choosing tech stacks that work well together and are well-understood by AI models. This guide helps you select proven combinations that minimize setup complexity and maximize development velocity.

Stack Selection Framework

1. Requirements Analysis

Before choosing technologies, thoroughly understand your requirements:

Functional Requirements

  • Core Features: What must the system do?
  • User Experience: Performance, accessibility, mobile support
  • Integration Needs: External APIs, legacy systems, third-party services
  • Data Requirements: Structure, volume, relationships, compliance

Non-Functional Requirements

  • Performance: Response times, throughput, scalability
  • Security: Authentication, authorization, data protection
  • Reliability: Uptime, error handling, disaster recovery
  • Maintainability: Code organization, documentation, testing

Constraints

  • Time: Development timeline and milestones
  • Budget: Development and operational costs
  • Team: Skills, experience, learning capacity
  • Infrastructure: Existing systems, deployment constraints

2. Decision Criteria Matrix

Use this framework to evaluate technology options:

evaluation_criteria:
  technical_fit:
    weight: 30%
    factors:
      - requirement_coverage
      - performance_characteristics
      - scalability_potential
      - integration_capabilities
  
  team_readiness:
    weight: 25%
    factors:
      - existing_expertise
      - learning_curve
      - documentation_quality
      - community_support
  
  project_constraints:
    weight: 20%
    factors:
      - development_speed
      - time_to_market
      - budget_constraints
      - resource_availability
  
  long_term_viability:
    weight: 15%
    factors:
      - technology_maturity
      - ecosystem_health
      - vendor_stability
      - migration_difficulty
  
  operational_considerations:
    weight: 10%
    factors:
      - deployment_complexity
      - monitoring_tools
      - security_features
      - maintenance_overhead

Technology Categories & Options

Frontend Technologies

React Ecosystem

stack:
  frontend: "React 18+"
  framework: "Next.js 14" # or "Vite + React Router"
  styling: "TailwindCSS" # or "styled-components", "CSS Modules"
  state: "Zustand" # or "Redux Toolkit", "React Query"

pros:
  - Largest ecosystem and community
  - Extensive tooling and libraries
  - Strong TypeScript support
  - Flexible architecture options

cons:
  - Steep learning curve for beginners
  - Choice paralysis (too many options)
  - Bundle size can grow large
  - Frequent ecosystem changes

best_for:
  - Complex interactive applications
  - Teams with JavaScript expertise
  - Projects requiring custom UI components
  - Long-term, scalable applications

Vue.js Ecosystem

stack:
  frontend: "Vue.js 3"
  framework: "Nuxt.js 3" # or "Vite + Vue Router"
  styling: "TailwindCSS" # or "Vuetify", "Quasar"
  state: "Pinia" # or "Vuex"

pros:
  - Gentle learning curve
  - Excellent documentation
  - Good performance out of the box
  - Progressive adoption friendly

cons:
  - Smaller ecosystem than React
  - Fewer job opportunities
  - Less enterprise adoption
  - TypeScript support improving but not native

best_for:
  - Teams new to modern frontend
  - Rapid prototyping
  - Medium-complexity applications
  - Progressive enhancement of existing sites

Other Frontend Options

svelte_kit:
  pros: ["Minimal bundle size", "Simple syntax", "Great performance"]
  cons: ["Small ecosystem", "Limited tooling", "Newer technology"]
  best_for: ["Performance-critical apps", "Small teams", "Simple to medium complexity"]

angular:
  pros: ["Full framework", "TypeScript-first", "Enterprise features"]
  cons: ["Steep learning curve", "Heavy framework", "Opinionated structure"]
  best_for: ["Large enterprise applications", "Teams from Java/.NET background"]

solid_js:
  pros: ["React-like syntax", "Superior performance", "No virtual DOM"]
  cons: ["Very new ecosystem", "Limited adoption", "Few libraries"]
  best_for: ["Performance-critical applications", "Experimental projects"]

Backend Technologies

Node.js Ecosystem

# API-focused backend
stack:
  runtime: "Node.js 18+"
  framework: "Express.js" # or "Fastify", "Koa"
  database_client: "Prisma" # or "TypeORM", "Sequelize"
  validation: "Zod" # or "Joi", "express-validator"
  auth: "Passport.js" # or "Auth0", "Firebase Auth"

pros:
  - JavaScript everywhere (shared code)
  - Large package ecosystem (npm)
  - Fast development cycle
  - Good for real-time applications

cons:
  - Single-threaded limitations
  - Memory usage for long-running processes
  - Callback complexity (though mitigated by async/await)
  - Security concerns with package dependencies

best_for:
  - APIs and microservices
  - Real-time applications (chat, collaboration)
  - Full-stack JavaScript teams
  - Rapid prototyping and MVPs

Python Ecosystem

# Web API backend
stack:
  runtime: "Python 3.11+"
  framework: "FastAPI" # or "Django", "Flask"
  database_client: "SQLAlchemy" # or "Django ORM", "asyncpg"
  validation: "Pydantic" # built into FastAPI
  auth: "python-jose" # or "Django Auth", "Authlib"

pros:
  - Excellent for data processing and ML
  - Clean, readable syntax
  - Strong typing with modern Python
  - Great scientific computing ecosystem

cons:
  - Slower execution than compiled languages
  - Global Interpreter Lock (GIL) limitations
  - Packaging complexity
  - Runtime dependency management

best_for:
  - Data-heavy applications
  - Machine learning integration
  - Scientific computing
  - Teams with Python expertise

Go Ecosystem

stack:
  runtime: "Go 1.21+"
  framework: "Gin" # or "Echo", "Fiber", "standard net/http"
  database_client: "GORM" # or "sqlx", "pgx"
  validation: "validator/v10"
  auth: "golang-jwt"

pros:
  - Excellent performance and concurrency
  - Simple deployment (single binary)
  - Strong standard library
  - Fast compilation

cons:
  - Less flexible than dynamic languages
  - Smaller ecosystem than Node.js/Python
  - Verbose error handling
  - Limited generics (improving)

best_for:
  - High-performance APIs
  - Microservices
  - Infrastructure tools
  - Teams prioritizing performance and simplicity

Rust Ecosystem

stack:
  runtime: "Rust stable"
  framework: "Axum" # or "Actix-web", "Warp"
  database_client: "SQLx" # or "Diesel", "Sea-ORM"
  serialization: "serde"
  auth: "jsonwebtoken"

pros:
  - Maximum performance and safety
  - Excellent concurrency
  - Zero-cost abstractions
  - Growing ecosystem

cons:
  - Steep learning curve
  - Longer development time initially
  - Smaller talent pool
  - Complex async programming

best_for:
  - Performance-critical systems
  - Systems programming
  - Teams willing to invest in learning
  - Long-running, resource-intensive services

Database Technologies

Relational Databases

postgresql:
  use_cases: ["Complex queries", "ACID compliance", "JSON support", "Large datasets"]
  pros: ["Feature-rich", "Excellent performance", "Strong consistency", "Extensible"]
  cons: ["Complex setup", "Resource intensive", "Requires SQL expertise"]
  
  recommended_for:
    - "E-commerce platforms"
    - "Financial applications"  
    - "Content management systems"
    - "Multi-tenant SaaS applications"

mysql:
  use_cases: ["Web applications", "Read-heavy workloads", "Simple queries"]
  pros: ["Easy setup", "Wide adoption", "Good performance", "Rich tooling"]
  cons: ["Limited features vs PostgreSQL", "Licensing considerations", "Less JSON support"]
  
  recommended_for:
    - "WordPress sites"
    - "Simple web applications"
    - "Legacy system integrations"

sqlite:
  use_cases: ["Development", "Small applications", "Embedded systems", "Prototypes"]
  pros: ["Zero configuration", "Single file", "Fast for small datasets", "Embedded"]
  cons: ["No concurrency", "Limited scalability", "No network access"]
  
  recommended_for:
    - "Development and testing"
    - "Desktop applications"
    - "Mobile applications"
    - "Configuration storage"

NoSQL Databases

mongodb:
  use_cases: ["Document storage", "Flexible schemas", "Rapid development", "JSON APIs"]
  pros: ["Flexible schema", "Horizontal scaling", "Rich queries", "JSON native"]
  cons: ["Eventual consistency", "Memory usage", "Learning curve", "ACID limitations"]
  
  recommended_for:
    - "Content management"
    - "User profiles and preferences"
    - "Product catalogs"
    - "Real-time analytics"

redis:
  use_cases: ["Caching", "Session storage", "Real-time features", "Message queues"]
  pros: ["Extremely fast", "Rich data types", "Pub/sub support", "Lua scripting"]
  cons: ["Memory only", "Data persistence complexity", "Single-threaded", "Memory limits"]
  
  recommended_for:
    - "Application caching"
    - "Session management"
    - "Real-time leaderboards"
    - "Message queuing"

Authentication & Authorization

Managed Solutions

auth0:
  pros: ["Full-featured", "Multiple providers", "Enterprise features", "Good docs"]
  cons: ["Cost", "Vendor lock-in", "Complex pricing", "External dependency"]
  best_for: ["Enterprise applications", "Multiple auth methods", "Complex requirements"]

clerk:
  pros: ["Developer-friendly", "React integration", "Modern UX", "Good free tier"]
  cons: ["Newer product", "Limited customization", "React-focused"]
  best_for: ["React applications", "B2C products", "Rapid development"]

supabase_auth:
  pros: ["Open source", "PostgreSQL integration", "Good free tier", "Full stack"]
  cons: ["Limited providers", "Newer product", "Supabase ecosystem only"]
  best_for: ["PostgreSQL apps", "Full Supabase stack", "Open source preference"]

Self-Hosted Solutions

keycloak:
  pros: ["Full-featured", "Open source", "Standards compliant", "Enterprise ready"]
  cons: ["Complex setup", "Resource heavy", "Learning curve", "Java-based"]
  best_for: ["Enterprise", "On-premises", "Multiple applications", "Standards compliance"]

custom_jwt:
  pros: ["Full control", "No external dependencies", "Customizable", "Cost-effective"]
  cons: ["Security responsibility", "Development time", "Maintenance burden"]
  best_for: ["Simple requirements", "High security needs", "Cost constraints"]

Stack Combinations & Patterns

Rapid Development Stacks

The “Supabase Stack”

stack:
  frontend: "Next.js + React"
  backend: "Supabase (PostgreSQL + Auth + Storage)"
  styling: "TailwindCSS"
  deployment: "Vercel"

strengths:
  - Very fast development
  - Managed services reduce complexity
  - Good developer experience
  - Integrated ecosystem

trade_offs:
  - Vendor lock-in
  - Limited customization
  - Pricing scaling
  - Less control over infrastructure

ideal_for:
  - MVPs and prototypes
  - Small to medium applications
  - Solo developers or small teams
  - B2C applications with standard requirements

The “T3 Stack”

stack:
  frontend: "Next.js + React + TypeScript"
  backend: "Next.js API routes + tRPC"
  database: "PostgreSQL + Prisma"
  auth: "NextAuth.js"
  styling: "TailwindCSS"

strengths:
  - Type-safe end-to-end
  - Modern development experience
  - Single language (TypeScript)
  - Good scaling path

trade_offs:
  - Full-stack JavaScript limitations
  - Monolithic deployment
  - Learning multiple libraries
  - Node.js performance constraints

ideal_for:
  - Full-stack TypeScript teams
  - Type safety prioritized
  - Medium complexity applications
  - Rapid iteration requirements

Enterprise Stacks

Microservices Architecture

stack:
  frontend: "React + TypeScript"
  api_gateway: "Kong or AWS API Gateway"
  services: "Go or Java Spring Boot"
  database: "PostgreSQL per service"
  message_queue: "Apache Kafka"
  service_mesh: "Istio"
  deployment: "Kubernetes"
  monitoring: "Prometheus + Grafana"

strengths:
  - Highly scalable
  - Team autonomy
  - Technology diversity
  - Fault isolation

trade_offs:
  - High complexity
  - DevOps overhead
  - Distributed system challenges
  - Longer development time

ideal_for:
  - Large organizations
  - Multiple teams
  - High scalability requirements
  - Complex business domains

Monolithic Enterprise

stack:
  frontend: "Angular + TypeScript"
  backend: "Spring Boot + Java"
  database: "PostgreSQL or Oracle"
  cache: "Redis"
  search: "Elasticsearch"
  auth: "Keycloak or LDAP"
  deployment: "Docker + Kubernetes or traditional servers"

strengths:
  - Proven architecture
  - Enterprise tooling
  - Strong consistency
  - Easier testing and debugging

trade_offs:
  - Scaling limitations
  - Technology coupling
  - Deployment coordination
  - Single points of failure

ideal_for:
  - Enterprise applications
  - Complex business logic
  - Strong consistency requirements
  - Established organizations

Performance-Focused Stacks

High-Performance API

stack:
  api: "Rust + Axum or Go + Gin"
  database: "PostgreSQL with connection pooling"
  cache: "Redis Cluster"
  load_balancer: "NGINX or HAProxy"
  deployment: "Kubernetes with horizontal scaling"

strengths:
  - Maximum performance
  - Efficient resource usage
  - Excellent concurrency
  - Low latency

trade_offs:
  - Development complexity
  - Smaller talent pool
  - Longer development time
  - Limited ecosystem

ideal_for:
  - High-traffic APIs
  - Real-time systems
  - Resource-constrained environments
  - Performance-critical applications

Mobile-First Stacks

React Native Stack

stack:
  mobile: "React Native + TypeScript"
  backend: "Node.js + Express or Serverless"
  database: "PostgreSQL or Firebase Firestore"
  auth: "Firebase Auth or Auth0"
  push_notifications: "Firebase Cloud Messaging"
  deployment: "EAS Build + App Store/Play Store"

strengths:
  - Cross-platform development
  - JavaScript expertise reuse
  - Large ecosystem
  - Hot reloading

trade_offs:
  - Performance vs native
  - Platform-specific issues
  - Bridge limitations
  - Debugging complexity

ideal_for:
  - Cross-platform mobile apps
  - JavaScript teams
  - Rapid prototyping
  - Standard mobile features

Decision Trees

Frontend Framework Selection

Do you need server-side rendering?
├─ Yes → Next.js (React) or Nuxt.js (Vue) or SvelteKit
└─ No → Single Page Application
    ├─ Team has React experience?
    │   ├─ Yes → React + Vite
    │   └─ No → Vue.js (easier learning curve)
    └─ Performance critical?
        ├─ Yes → Svelte or SolidJS
        └─ No → React or Vue.js

Backend Technology Selection

What's your primary use case?
├─ API/Microservices
│   ├─ High performance needed?
│   │   ├─ Yes → Go or Rust
│   │   └─ No → Node.js or Python
│   └─ Team expertise?
│       ├─ JavaScript → Node.js
│       ├─ Python → FastAPI or Django
│       └─ New team → Go (simple) or Node.js (popular)
│
├─ Data Processing/ML
│   └─ Python (with FastAPI for APIs)
│
└─ Enterprise Application
    ├─ Existing Java infrastructure → Spring Boot
    ├─ Microsoft ecosystem → .NET Core
    └─ Modern startup → Node.js or Go

Database Selection

What's your data structure?
├─ Well-defined relationships → PostgreSQL
├─ Flexible/evolving schema → MongoDB
├─ Simple key-value → Redis
└─ File-like documents → S3 + metadata DB

What's your scale?
├─ < 1GB, single server → SQLite
├─ < 100GB, moderate traffic → PostgreSQL
├─ > 100GB or high write volume → Sharded PostgreSQL or MongoDB
└─ Massive scale → Consider specialized databases

Stack Validation Checklist

Technical Validation

  • Performance Requirements: Can the stack meet response time and throughput needs?
  • Scalability: How will it handle growth in users and data?
  • Security: Does it support required security features?
  • Integration: Can it integrate with existing systems?
  • Data Requirements: Does it handle your data volume and complexity?

Team Validation

  • Expertise: Does the team have experience with these technologies?
  • Learning Curve: How long to become productive?
  • Hiring: Can you find developers with these skills?
  • Documentation: Is there good learning material available?
  • Community: Is there active community support?

Business Validation

  • Time to Market: How quickly can you deliver?
  • Development Cost: What’s the resource investment?
  • Operational Cost: What are the ongoing expenses?
  • Vendor Risk: Are you too dependent on any single vendor?
  • Long-term Viability: Will this technology be supported long-term?

Risk Assessment

  • Single Points of Failure: What happens if a key technology fails?
  • Migration Path: How difficult would it be to change later?
  • Security Vulnerabilities: What are the known risks?
  • Performance Bottlenecks: Where might performance degrade?
  • Operational Complexity: What’s required to run this in production?

Common Anti-Patterns

Technology-Driven Decisions

❌ “Let’s use Kubernetes because it’s modern” ✅ “Let’s evaluate if Kubernetes solves our scaling and deployment needs”

Resume-Driven Development

❌ “I want to learn Rust, let’s use it for this project” ✅ “Rust’s performance benefits align with our high-throughput requirements”

Following Hype Blindly

❌ “Everyone’s using microservices, we should too” ✅ “Our team size and complexity justify microservices architecture”

Over-Engineering

❌ Building for problems you don’t have yet ✅ Starting simple and scaling complexity as needed

Under-Engineering

❌ Ignoring known future requirements ✅ Planning for reasonable growth and evolution

Stack Evolution Strategy

Phase 1: MVP/Prototype

  • Choose familiar technologies
  • Prioritize development speed
  • Accept some technical debt
  • Focus on validation

Phase 2: Product-Market Fit

  • Address performance bottlenecks
  • Improve security and reliability
  • Add proper monitoring
  • Refactor major pain points

Phase 3: Scale

  • Optimize for performance and cost
  • Add redundancy and fault tolerance
  • Implement proper DevOps practices
  • Consider architectural changes

Phase 4: Enterprise

  • Add compliance and governance
  • Implement advanced monitoring
  • Create disaster recovery plans
  • Document everything

Conclusion

Technology stack selection is a strategic decision that impacts your project’s success. Use this systematic approach:

  1. Understand Requirements - Functional, non-functional, and constraints
  2. Evaluate Options - Use the decision criteria matrix
  3. Validate Choices - Technical, team, and business validation
  4. Plan Evolution - How the stack will grow with your needs
  5. Document Decisions - Record rationale for future reference

Remember: There’s no perfect stack, only trade-offs. Choose the stack that best balances your current needs with future flexibility.