Architecture Assessment
Evaluating system architecture, scalability, and design patterns
Architecture assessment evaluates how software systems are structured and whether they can support current and future business needs. Poor architecture is one of the most expensive problems to fix post-acquisition—often requiring multi-million dollar rewrites that take years to complete.
Why Architecture Matters in M&A
Software architecture determines the fundamental constraints of what's possible:
| Architecture Quality | Business Impact | M&A Implication |
|---|---|---|
| Scalability | Can the system handle 10x growth? | Growth ceiling = valuation ceiling |
| Reliability | What's the uptime? MTTR? | Customer churn risk, SLA penalties |
| Maintainability | How fast can changes be made? | Feature velocity, engineering costs |
| Flexibility | Can new capabilities be added? | Integration difficulty, synergy timeline |
| Cost Efficiency | Infrastructure spend per user | Gross margin impact |
Real Example: A PE firm acquired a logistics SaaS for $120M. During integration, they discovered the "microservices" architecture was actually a distributed monolith—services were so coupled that any change required coordinated deployments across 15 services. Feature velocity dropped 60% post-acquisition, and they eventually spent $8M on a re-architecture project.
Architecture Patterns: What to Look For
Monolithic Architecture
| Aspect | Details |
|---|---|
| Characteristics | Single deployable unit, shared database, all code in one repository |
| Deployment | All-or-nothing; entire app redeploys for any change |
| Scaling | Vertical (bigger servers) or full horizontal replication |
| Team Structure | Everyone works in same codebase |
When It's Fine:
- Companies with <$10M ARR and <20 engineers
- Products with <100K monthly active users
- Applications with relatively simple domains
- Well-structured monoliths with clear internal modules
When It's Concerning:
- Deployment takes >30 minutes
- Simple changes require full regression testing
- Teams step on each other's code constantly
- Cannot scale specific components independently
Microservices Architecture
| Aspect | Details |
|---|---|
| Characteristics | Multiple independent services, separate databases, API communication |
| Deployment | Each service deploys independently |
| Scaling | Scale individual services based on demand |
| Team Structure | Teams own specific services end-to-end |
When It's Appropriate:
- Large engineering teams (50+ engineers)
- Complex domains with clear bounded contexts
- High scale requirements (millions of users)
- Need for technology diversity
Warning Signs of Poor Implementation:
- Distributed Monolith: Services that must be deployed together
- Chatty Services: Excessive inter-service communication
- Shared Databases: Multiple services accessing same tables
- No Service Ownership: Nobody knows who owns what
- Inconsistent Patterns: Each service built differently
Modular Monolith (Often the Best Choice)
Single deployment with well-defined internal module boundaries. Combines monolith simplicity with microservices maintainability.
Ideal For: Companies planning to scale, with clear domain boundaries, but not yet at microservices-required scale.
Architecture Assessment Framework
1. Component Analysis
Map the major components and their relationships:
- What are the main system components?
- How do they communicate (REST, GraphQL, message queues, direct DB)?
- What are the dependency directions?
- Are there circular dependencies?
2. Data Architecture
Understand how data flows and is stored:
- Database Technology: SQL vs NoSQL vs hybrid—appropriate for use case?
- Data Ownership: Which services own which data?
- Consistency Model: Strong vs eventual consistency—appropriate trade-offs?
- Data Isolation: Multi-tenant architecture security
3. Scalability Analysis
| Dimension | Questions to Ask | Red Flag |
|---|---|---|
| Horizontal Scaling | Can you add more instances? | Stateful services, session affinity required |
| Database Scaling | Read replicas? Sharding possible? | Single DB instance, no read replicas |
| Caching | What's cached? Cache invalidation? | No caching layer, or cache causing bugs |
| Async Processing | Background jobs? Message queues? | All processing synchronous in request |
4. Reliability Patterns
- Circuit Breakers: Do failing dependencies cascade?
- Retry Logic: Handled gracefully or causing thundering herds?
- Timeouts: Are they configured? Reasonable values?
- Graceful Degradation: Does partial failure = total failure?
- Health Checks: Can load balancers detect unhealthy instances?
5. Integration Architecture
- Third-party integrations and their criticality
- API design quality (REST maturity, consistency)
- Event-driven vs request-driven patterns
- Integration failure handling
Architecture Red Flags and Costs
| Red Flag | Why It Matters | Remediation Cost |
|---|---|---|
| Circular dependencies | Changes cascade unpredictably, deployment nightmares | $200K - $1M |
| Database as integration layer | Tight coupling, scaling impossible | $500K - $3M |
| No API versioning | Breaking changes affect all clients | $50K - $200K |
| Synchronous everything | Performance bottlenecks, timeout cascades | $100K - $500K |
| God class/service | Single component doing everything | $300K - $2M |
| Technology obsolescence | Framework/language end of life | $1M - $10M |
| No documentation | Only original developers understand system | $50K - $200K |
Scalability Reality Check
Ask the target company these questions:
- "What happens if traffic doubles tomorrow?" — Listen for confidence vs. concern
- "What's your biggest scalability bottleneck?" — Honest teams know their limits
- "Have you load tested? What were the results?" — No testing = unknown limits
- "What's your database size and growth rate?" — Project forward to identify cliffs
Architecture Documentation Expectations
Mature organizations should have:
- System Context Diagram: How the system fits in the broader ecosystem
- Container Diagram: Major deployable units and their interactions
- Component Diagram: Key components within each container
- Data Flow Diagram: How data moves through the system
- Deployment Diagram: How components map to infrastructure
Missing documentation isn't just a red flag—it's a cost. Estimate $100K-$300K in knowledge recovery and documentation creation.