Microservices vs Monoliths Part 5: Common Pitfalls and How to Avoid Them

This is Part 5, the final installment in our series on microservices versus monoliths. We've covered motivations (Part 1), business drivers (Part 2), technical considerations (Part 3), and a practical framework (Part 4). Now we'll examine the common pitfalls that trap even experienced teams.

Common Pitfalls to Avoid

Regardless of which architecture you choose, certain pitfalls arise frequently. Understanding these helps you recognize warning signs early and course-correct before problems become entrenched.

Distributed Monoliths

One of the most painful architectural patterns I encounter is the "distributed monolith" — a system broken into separate services that remain tightly coupled and, of course, must be deployed together. This combines the operational complexity of microservices with the change coordination challenges of monoliths.

The signs of a distributed monolith:

• Services that must be deployed together in a specific sequence
• Shared databases across multiple services¹
• Tight coupling through synchronous HTTP calls in long chains
• Changes that consistently require modifications to multiple services
• No clear service boundaries or ownership

"If your services can't be deployed independently, you've paid the microservice tax without receiving the benefit," I caution teams considering this approach.

How to avoid it: Focus on service independence from day one. Each service should have its own database, clear boundaries, and well-defined APIs. If you find services consistently need to change together, they probably belong in the same service.

Premature Decomposition

Breaking apart a system before domain boundaries are well understood often leads to services that don't align with how the business actually functions. This results in services that are constantly reaching across boundaries for data or functionality.

Premature decomposition typically happens when teams:

• Start with microservices before understanding their domain
• Decompose based on technical layers rather than business capabilities²
• Create services around individual entities rather than bounded contexts
• Split services too finely, creating a "nanoservices" architecture

How to avoid it: Start with a modular monolith that establishes clear boundaries. Let these boundaries solidify through actual usage and understanding of your domain before extracting them into services. The seams where you'd split your monolith will become obvious over time.

Ignoring Data Gravity

Data has gravity — functionality tends to cluster around data. Many teams underestimate how challenging it becomes to maintain data consistency across service boundaries, especially when transactions span multiple services.

Common data-related mistakes:

• Creating services that need real-time access to other services' data
• Sharing databases across services (creating coupling)
• Implementing complex distributed transactions without understanding the implications³
• Failing to consider eventual consistency requirements⁴

How to avoid it: Design services around data ownership. Each service should own its data completely. When services need data from other services, prefer eventual consistency through events rather than synchronous calls. Accept that some use cases might not be suitable for microservices if they require strong consistency across multiple domains.

Underinvesting in Developer Experience

Both architectures require investment in developer tooling, but in different ways. Monoliths need excellent modularity enforcement and test isolation. Microservices require strong local development environments, service mocks, and deployment automation.

The cost of poor developer experience:

• Developers spend hours setting up their environment instead of writing code
• Debugging requires coordination across multiple teams
• Testing becomes so painful that developers skip it
• Deployment becomes a feared event rather than routine

How to avoid it: Invest in developer experience from the start. For monoliths, this means automated testing, clear module boundaries, and fast build times. For microservices, it means service virtualization, local development tools, and comprehensive documentation.

Neglecting Operational Concerns

Teams often focus on the development phase and underestimate the ongoing operational overhead of their chosen architecture.

For monoliths, this includes:

• Build times that grow increasingly slow
• Deployment windows that become longer and riskier
• Difficulty isolating failures
• Challenges with independent scaling

For microservices, this includes:

• Debugging issues across service boundaries
• Managing multiple deployment pipelines
• Monitoring and tracing distributed requests
• Handling network failures and timeouts

How to avoid it: Make operations a first-class concern from day one. For microservices, don't deploy your second service until you have proper monitoring, tracing, and deployment automation for your first. For monoliths, establish clear module boundaries and enforce them with automated tools.

Following the Wrong Success Metrics

Teams often optimize for the wrong metrics when evaluating their architectural decisions.

Poor metrics:

• Number of microservices (more isn't better)
• Lines of code per service (smaller isn't necessarily better)
• Technology diversity (using different languages for each service doesn't provide value)

Better metrics:⁵

• Deployment frequency and lead time
• Mean time to recovery from failures
• Developer productivity and satisfaction
• System reliability and availability
• Cost per transaction or user

How to avoid it: Define success metrics upfront that align with business goals. Measure these consistently and be willing to change course if the architecture isn't delivering the expected benefits.

The "Rewrite" Trap

Organizations with struggling monoliths sometimes see a microservices rewrite as salvation. However, large-scale rewrites rarely succeed, regardless of target architecture.

The rewrite trap typically unfolds like this:

1. Team decides existing monolith is unsalvageable
2. Plans ambitious microservices rewrite
3. Business continues requesting features in old system
4. Team maintains both systems, splitting attention
5. Rewrite takes much longer than expected
6. Team carries forward problems from old system
7. New system launches with bugs and missing features

How to avoid it: Favor incremental evolution over big rewrites. Identify the most valuable or problematic parts of your system and extract or rewrite those first. Ship them to production. Learn from the experience. Then tackle the next piece. This approach maintains business continuity while making steady progress.

The Path Forward

Architecture isn't a one-time decision but an ongoing practice. The most successful teams regularly reassess their architectural choices, remain open to evolution, and stay focused on business outcomes rather than architectural purity.

Whether you choose microservices, monoliths, or something in between, success comes from:

1. Clear understanding of your business drivers and technical constraints
2. Honest assessment of your team's capabilities and readiness
3. Continuous evaluation of whether your architecture serves your needs
4. Willingness to evolve when circumstances change
5. Focus on outcomes rather than architectural trends

The right architecture enables your team to deliver value to customers efficiently and sustainably. Everything else is secondary.

Series Conclusion

Throughout this five-part series, we've explored how to make intelligent decisions about microservices versus monolith architecture:

• Part 1 examined the hype and hidden motivations behind architectural choices
• Part 2 identified the business drivers that should guide decisions
• Part 3 covered the technical considerations that matter most
• Part 4 provided a practical decision framework
• Part 5 (this post) outlined common pitfalls and how to avoid them

The fundamental lesson: architectural decisions should serve business goals, not the other way around. By understanding your constraints, being honest about your capabilities, and focusing on delivering value, you can make choices that set your organization up for success — regardless of whether you choose microservices, monoliths, or something in between.

Footnotes:

1. Evans, Eric. Domain-Driven Design: Tackling Complexity in the Heart of Software. Boston: Addison-Wesley, 2004. ↩
2. Parnas, David L. "On the Criteria to Be Used in Decomposing Systems into Modules." Communications of the ACM 15, no. 12 (1972): 1053–58. ↩
3. Garcia-Molina, Hector, and Kenneth Salem. "Sagas." In Proceedings of the 1987 ACM SIGMOD International Conference on Management of Data, 249–59. New York: Association for Computing Machinery, 1987. ↩
4. Vogels, Werner. "Eventually Consistent." Communications of the ACM 52, no. 1 (2009): 40–44. ↩
5. Humble, Jez, and David Farley. Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Upper Saddle River, NJ: Addison-Wesley, 2010. ↩