# FraiseQL Audiences & User Types

**Last Updated**: October 23, 2025

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## 🎯 Primary Audience: Production Teams

**FraiseQL is designed for production teams** building GraphQL APIs with PostgreSQL. Our primary users are developers and teams who need high-performance, database-native GraphQL APIs.

### **Target Profile**
- Teams with 2-50 developers
- Building customer-facing APIs
- Using PostgreSQL as primary database
- Need sub-millisecond query performance
- Require enterprise features (monitoring, security, scalability)

---

## 👥 User Types & Paths

### **1. 🚀 Beginners** - New to GraphQL/Python/PostgreSQL

#### **Profile**
- First time building GraphQL APIs
- Basic Python knowledge
- New to PostgreSQL or databases
- Learning API development

#### **Assumed Knowledge**
- ✅ Basic programming concepts
- ✅ Simple SQL queries
- ❌ GraphQL schema design
- ❌ Database optimization
- ❌ API performance tuning

#### **Goals**
- Build first GraphQL API
- Understand basic concepts
- Deploy working application
- Learn best practices

#### **Recommended Path**
```bash
# Start here - 5 minute working API
fraiseql init my-api
cd my-api
fraiseql run

# Then explore examples
cd examples/blog_simple/
```

#### **Success Criteria**
- ✅ Working GraphQL API in < 30 minutes
- ✅ Understand basic queries/mutations
- ✅ Deployed to development environment
- ✅ Can read/modify simple resolvers

---

### **2. 🏭 Production Teams** - Deploying to Production

#### **Profile**
- Experienced developers/engineers
- Building customer-facing applications
- Need enterprise-grade features
- Performance and reliability critical
- Team of 2-50 developers

#### **Assumed Knowledge**
- ✅ GraphQL API development
- ✅ PostgreSQL database design
- ✅ Python web frameworks
- ✅ Production deployment
- ✅ Performance monitoring

#### **Goals**
- High-performance GraphQL APIs
- Enterprise features (APQ, caching, monitoring)
- Database-native architecture
- Zero external dependencies
- Production reliability

#### **Recommended Path**
```bash
# Production installation
pip install fraiseql[enterprise]

# Start with enterprise examples
cd examples/ecommerce/
# or
cd examples/blog_enterprise/

# Study performance guide
open docs/performance/
```

#### **Success Criteria**
- ✅ < 1ms P95 query latency
- ✅ 99.9% cache hit rate
- ✅ Enterprise monitoring integrated
- ✅ Zero-downtime deployments
- ✅ Database-native caching

---

### **3. 🤝 Contributors** - Improving FraiseQL

#### **Profile**
- Experienced Python/Rust developers
- Interested in database frameworks
- Want to contribute to open source
- Understand system architecture

#### **Assumed Knowledge**
- ✅ Advanced Python development
- ✅ Rust programming
- ✅ Database internals
- ✅ GraphQL specification
- ✅ Open source contribution

#### **Goals**
- Fix bugs and add features
- Improve performance
- Enhance documentation
- Review pull requests
- Maintain code quality

#### **Recommended Path**
```bash
# Development setup
git clone https://github.com/fraiseql/fraiseql
cd fraiseql
pip install -e .[dev]

# Start contributing
open CONTRIBUTING.md
open docs/core/architecture.md
```

#### **Success Criteria**
- ✅ First PR merged
- ✅ Understand codebase architecture
- ✅ Can debug performance issues
- ✅ Familiar with testing patterns
- ✅ Code review confidence

---

## 📚 Content Organization by Audience

### **Beginner Content**
- ✅ Quickstart guides
- ✅ Basic examples
- ✅ Concept explanations
- ✅ Step-by-step tutorials
- ❌ Advanced performance tuning
- ❌ Enterprise features

### **Production Content**
- ✅ Performance guides
- ✅ Enterprise features
- ✅ Deployment patterns
- ✅ Monitoring integration
- ✅ Migration guides
- ❌ Basic tutorials

### **Contributor Content**
- ✅ Architecture documentation
- ✅ Code patterns
- ✅ Testing strategies
- ✅ Development workflows
- ✅ API design decisions
- ❌ User tutorials

---

## 🎯 "Is This For Me?" Decision Tree

### **Quick Assessment**

**Are you building a GraphQL API with PostgreSQL?**
- **Yes** → Continue
- **No** → FraiseQL may not be the right fit

**What's your experience level?**

#### **Beginner** (0-2 years API development)
- Choose if: Learning GraphQL, first PostgreSQL project, need simple API
- Start with: Quickstart → Basic examples

#### **Intermediate** (2-5 years)
- Choose if: Building production APIs, need performance, team deployment
- Start with: Enterprise examples → Performance guide

#### **Advanced** (5+ years)
- Choose if: Contributing to frameworks, optimizing databases, building tools
- Start with: Architecture docs → Contributing guide

---

## 📖 Documentation Tags

All documentation pages are tagged by primary audience:

- 🟢 **Beginner** - Basic concepts, tutorials, getting started
- 🟡 **Production** - Performance, deployment, enterprise features
- 🔴 **Contributor** - Architecture, development, contribution

### **Example Tags**
```
🟢 Beginner · 🟡 Production
# Quickstart Guide

Content for beginners and production users...
```

---

## 🚀 Getting Started by Audience

### **For Beginners**
```bash
# 5-minute API
fraiseql init my-first-api
cd my-first-api
fraiseql run

# Learn concepts
open docs/core/concepts-glossary.md
open examples/blog_simple/
```

### **For Production Teams**
```bash
# Enterprise setup
pip install fraiseql[enterprise]

# Performance-focused examples
open examples/ecommerce/
open docs/performance/
open docs/production/
```

### **For Contributors**
```bash
# Development environment
git clone https://github.com/fraiseql/fraiseql
cd fraiseql
make setup-dev

# Deep dive
open docs/core/architecture.md
open CONTRIBUTING.md
```

---

## 💡 Audience-Specific Features

### **Beginner-Friendly**
- Simple CLI commands
- Auto-generated boilerplate
- Clear error messages
- Progressive complexity
- Extensive examples

### **Production-Ready**
- Enterprise monitoring
- High-performance caching
- Database-native features
- Zero external dependencies
- Comprehensive testing

### **Contributor-Friendly**
- Clean architecture
- Comprehensive tests
- Clear documentation
- Modern tooling
- Performance benchmarks

---

*Audience definitions help users find relevant content quickly and set appropriate expectations for their skill level.*</content>
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