# tradeoff-monolith-vs-microservices

## Why It Matters

Choosing between monolith and microservices is one of the most important architectural decisions. Getting it wrong can lead to wasted resources, unnecessary complexity, or inability to scale. Understanding the trade-offs helps make informed decisions.

## Decision Framework

### Choose Monolith When:

- Team size is small (1-10 developers)
- Domain complexity is low to moderate
- Time-to-market is critical
- Building MVP or prototype
- Scaling requirements are predictable
- Prefer simplicity over flexibility
- Budget is limited

### Choose Microservices When:

- Multiple teams working independently
- Domain complexity is high
- Different parts have different scaling needs
- Different parts need different technologies
- Deployment frequency varies by feature
- Need fault isolation
- Budget allows for infrastructure overhead

## Monolith: Advantages & Disadvantages

### Advantages

**✅ Simpler Development**

- Single codebase to understand
- No inter-service communication
- Shared libraries and utilities
- Easier onboarding for new developers

**✅ Easier Deployment**

- Single deployable unit
- Simple CI/CD pipeline
- No distributed system issues
- Faster deployment time

**✅ Lower Operational Cost**

- Less infrastructure
- Fewer moving parts
- Simpler monitoring
- Lower cloud costs initially

**✅ Faster Development**

- No need for service discovery
- No distributed transactions
- Simpler debugging
- Easier testing

### Disadvantages

**❌ Scalability Limitations**

- Scale entire application, not individual parts
- Can't optimize hotspots independently
- May need to scale for one feature's load

**❌ Technology Lock-in**

- Must use single technology stack
- Hard to adopt new technologies
- May miss better tools for specific problems

**❌ Maintenance Challenges**

- Codebase grows large and complex
- Harder to understand over time
- More merge conflicts
- Longer CI/CD pipelines

**❌ Team Scalability**

- More developers → more conflicts
- Harder to parallelize work
- Single codebase bottleneck

## Microservices: Advantages & Disadvantages

### Advantages

**✅ Independent Scaling**

- Scale individual services based on need
- Optimize performance hotspots
- Cost-effective scaling
- Better resource utilization

**✅ Technology Diversity**

- Choose best tool for each service
- Adopt new technologies easily
- Use polyglot persistence
- Different languages and frameworks

**✅ Fault Isolation**

- One service failure doesn't crash everything
- Easier to identify issues
- Better reliability
- Circuit breakers and fallbacks

**✅ Team Autonomy**

- Teams own services independently
- Deploy without coordination
- Choose own technology
- Faster development cycles

**✅ Better Maintainability**

- Smaller codebases per service
- Easier to understand
- Less merge conflicts
- Easier to refactor

### Disadvantages

**❌ Increased Complexity**

- Distributed system challenges
- Inter-service communication
- Service discovery needed
- Distributed tracing essential

**❌ Operational Overhead**

- More infrastructure to manage
- Multiple deployments
- Complex monitoring
- Higher cloud costs

**❌ Data Consistency**

- No ACID transactions across services
- Eventual consistency
- Compensating transactions
- Saga pattern needed

**❌ Testing Complexity**

- Integration tests more complex
- Mock external services
- Test environment setup
- Flaky distributed tests

## Comparison Table

| Aspect                | Monolith                        | Microservices                  |
| --------------------- | ------------------------------- | ------------------------------ |
| **Development Speed** | Fast initially, slows over time | Slower initially, faster later |
| **Scalability**       | Scale everything                | Scale individual services      |
| **Technology**        | Single stack                    | Multiple technologies          |
| **Deployment**        | Single deployable unit          | Multiple deployable units      |
| **Complexity**        | Low                             | High                           |
| **Cost**              | Low initially                   | Higher overhead                |
| **Fault Isolation**   | Poor                            | Good                           |
| **Data Consistency**  | Strong ACID                     | Eventual consistency           |
| **Team Size**         | Best for small teams            | Best for large teams           |
| **Testing**           | Simple                          | Complex                        |

## Real-World Examples

### Start with Monolith

```
Company: Startup with 5 developers
Domain: Simple e-commerce platform
Decision: Monolith
Reason: Fast time-to-market, low complexity, single team

Result: Shipped in 3 months, 10K users
Next step: Evaluate split when team grows or complexity increases
```

### Migrate to Microservices

```
Company: Growing startup with 30 developers
Domain: Complex financial platform
Decision: Microservices
Reason: Multiple teams, different scaling needs, diverse technologies

Result: 12 services, independent deployments, faster iteration
Challenge: Distributed debugging, monitoring overhead
```

### Hybrid Approach

```
Company: Enterprise with 100 developers
Domain: Healthcare platform
Decision: Modular monolith for core, microservices for integrations

Result:
- Core logic in monolith (team efficiency)
- External integrations as microservices (flexibility)
- Gradual migration path
```

## Migration Path: Monolith → Microservices

### 1. Modular Monolith (First Step)

```sruja
Application = system "Application" {
  UserModule = container "User Module" { ... }
  OrderModule = container "Order Module" { ... }
  PaymentModule = container "Payment Module" { ... }
}
```

### 2. Extract Single Service

```sruja
PaymentService = container "Payment Service" {
  technology "Python"
  description "Extracted from monolith"
}

Monolith = container "Core Application" {
  technology "Node.js"
  description "Remaining functionality"
}

Monolith -> PaymentService "REST API"
```

### 3. Full Microservices

```sruja
UserService = container "User Service" { ... }
OrderService = container "Order Service" { ... }
PaymentService = container "Payment Service" { ... }
InventoryService = container "Inventory Service" { ... }
```

## Decision Checklist

### Before Choosing Monolith, Ensure:

- [ ] Team size < 10 developers
- [ ] Domain is well-understood
- [ ] Time-to-market is priority
- [ ] Scaling needs are predictable
- [ ] Single technology stack is acceptable
- [ ] Budget is limited

### Before Choosing Microservices, Ensure:

- [ ] Multiple teams working independently
- [ ] Domain has clear bounded contexts
- [ ] Different scaling needs per component
- [ ] Budget allows infrastructure overhead
- [ ] Team has distributed systems expertise
- [ ] Monitoring and observability tools in place

## Additional Context

This trade-off is not binary. Many successful systems use:

- Modular monoliths (clear boundaries, single deployable)
- Service-oriented architectures (few services, shared database)
- Event-driven architectures (microservices, async communication)

Related rules:

- `pattern-monolith` - When to use monolithic architecture
- `pattern-microservices` - When to use distributed architecture
- `tradeoff-sync-vs-async` - Choosing communication patterns
- `principle-separation` - Even monoliths need separation

## References

- Building Microservices by Sam Newman
- Monolith to Microservices Evolution
- The Fallacies of Distributed Computing
- Domain-Driven Design by Eric Evans
- Microservices Patterns by Chris Richardson