architecture-pattern-selector

Recommend architecture patterns (monolith, microservices, serverless, modular monolith) based on scale, team size, and constraints. Use when cto-architect needs to select the right architectural approach for a new system or migration.

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Install skill "architecture-pattern-selector" with this command: npx skills add alirezarezvani/claude-cto-team/alirezarezvani-claude-cto-team-architecture-pattern-selector

Architecture Pattern Selector

Provides systematic framework for selecting the right architecture pattern based on real-world constraints rather than hype or resume-driven development.

When to Use

  • Designing a new system from scratch
  • Evaluating whether to migrate from monolith to microservices
  • Choosing between serverless vs container-based architecture
  • Assessing if current architecture matches actual needs

Architecture Patterns

1. Monolith

Best for: Early-stage products, small teams, rapid iteration

Characteristics:

  • Single deployable unit
  • Shared database
  • Simple deployment and debugging
  • Fastest time to market

Choose when:

  • Team < 10 engineers
  • < 100K users
  • Product-market fit not yet proven
  • Need to iterate quickly on features
  • Limited DevOps expertise

Avoid when:

  • Different components need independent scaling
  • Multiple teams need to deploy independently
  • Parts of system have very different reliability requirements

2. Modular Monolith

Best for: Growing products that need structure without microservices complexity

Characteristics:

  • Single deployable unit with clear module boundaries
  • Modules communicate through defined interfaces
  • Can evolve to microservices later
  • Best of both worlds for mid-size teams

Choose when:

  • Team 10-30 engineers
  • 100K-1M users
  • Need better code organization
  • Want microservices benefits without operational overhead
  • Planning future extraction to services

Avoid when:

  • Need independent deployment per module
  • Modules have fundamentally different scaling requirements
  • Already have microservices expertise and infrastructure

3. Microservices

Best for: Large organizations with mature DevOps practices

Characteristics:

  • Independent deployable services
  • Service-specific databases
  • Complex operational requirements
  • High organizational overhead

Choose when:

  • Team > 30 engineers with multiple squads
  • 1M users with varying load patterns

  • Different services need different scaling strategies
  • Strong DevOps/Platform team
  • Clear bounded contexts

Avoid when:

  • Team lacks Kubernetes/container orchestration experience
  • No dedicated platform/DevOps team
  • Bounded contexts are unclear
  • Chasing microservices for resume value

4. Serverless

Best for: Event-driven workloads, variable traffic, cost optimization

Characteristics:

  • Pay-per-execution pricing
  • Auto-scaling to zero
  • Vendor lock-in concerns
  • Cold start latency

Choose when:

  • Highly variable or spiky traffic
  • Event-driven processing (webhooks, queues)
  • Cost-sensitive with unpredictable load
  • Simple request-response patterns
  • Limited DevOps capacity

Avoid when:

  • Consistent high-volume traffic (cost inefficient)
  • Low-latency requirements (< 100ms consistently)
  • Long-running processes
  • Complex stateful workflows

5. Hybrid

Best for: Complex systems with varied requirements

Characteristics:

  • Mix of patterns for different components
  • Monolith core with serverless functions
  • Microservices for specific bounded contexts

Choose when:

  • Different parts of system have different characteristics
  • Migrating incrementally from monolith
  • Some components need independent scaling
  • Event-driven workflows alongside synchronous APIs

Selection Framework

Step 1: Assess Current State

Team Size:        [ ] < 10   [ ] 10-30   [ ] 30-100   [ ] > 100
User Scale:       [ ] < 100K [ ] 100K-1M [ ] 1M-10M   [ ] > 10M
DevOps Maturity:  [ ] None   [ ] Basic   [ ] Intermediate [ ] Advanced
Deployment Freq:  [ ] Monthly [ ] Weekly [ ] Daily    [ ] Multiple/day

Step 2: Evaluate Constraints

ConstraintImpact on Pattern Choice
Time to marketFavors monolith
Team autonomyFavors microservices
Cost sensitivityFavors serverless or monolith
Latency requirementsDisfavors serverless
Compliance/securityMay require isolation (microservices)

Step 3: Apply Decision Matrix

See decision-matrix.md for the scoring framework.

Step 4: Consider Migration Path

Every architecture should have a migration path:

  • Monolith → Modular Monolith → Microservices
  • Monolith → Hybrid (extract specific services)
  • Serverless → Containers (when scale justifies)

Output Format

When recommending an architecture pattern, provide:

## Architecture Recommendation

### Recommended Pattern: [Pattern Name]
**Confidence**: High / Medium / Low

### Why This Pattern
[2-3 specific reasons based on constraints]

### Trade-offs Accepted
- [Trade-off 1 and why it's acceptable]
- [Trade-off 2 and why it's acceptable]

### Migration Path
- **Current**: [Current state]
- **Phase 1**: [Near-term architecture]
- **Phase 2**: [If/when to evolve]
- **Trigger**: [What would cause migration to next phase]

### Patterns Rejected
| Pattern | Reason Not Selected |
|---------|---------------------|
| [Pattern] | [Specific reason] |

### Implementation Considerations
- [Key consideration 1]
- [Key consideration 2]
- [Key consideration 3]

Anti-Patterns to Avoid

Resume-Driven Architecture

Choosing microservices because it looks good on resumes, not because the problem requires it.

Red flag: "We should use microservices because that's what Netflix uses." Reality check: You're not Netflix. What's your actual scale and team size?

Premature Distribution

Distributing a system before understanding the domain boundaries.

Red flag: "Let's start with microservices from day one." Reality check: You'll draw the wrong boundaries. Start monolith, extract when clear.

Complexity Worship

Equating complexity with sophistication.

Red flag: "Our architecture needs to be enterprise-grade." Reality check: Simple architectures that work beat complex ones that don't.


References

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