Microservices are like fire: handled correctly, they power your engine; handled poorly, they burn your house down. Most “microservices failures” aren’t failures of the technology, but of the architecture and boundaries.

In this guide, we’ll catalog the most common microservices anti-patterns and how to steer clear of them.

1. The Distributed Monolith

This is the “King of Anti-Patterns.” You have separate services, but they are so tightly coupled that you cannot deploy one without deploying them all.

• Symptoms: One service’s change breaks three others; long deployment “trains”; synchronous calls everywhere.
• The Fix: Use Asynchronous Messaging (RabbitMQ, Kafka) and adhere to Bounded Contexts. If two services always change together, they should probably be one service.

graph LR
    subgraph Anti-Pattern: Distributed Monolith
    A[Svc A] -- "Sync Call" --> B[Svc B]
    B -- "Sync Call" --> C[Svc C]
    end
    
    subgraph Best Practice: Decoupled
    D[Svc D] -- "Event" --> Bus((Message Bus))
    Bus -- "Consumer" --> E[Svc E]
    Bus -- "Consumer" --> F[Svc F]
    end

2. Nano-services

The opposite of a monolith, where your services are too small. One “service” might just be a single CRUD function.

• Symptoms: High network latency; impossible to manage 100+ repos for a 5-person team; extreme operational overhead.
• The Fix: Aim for Business Capabilities, not functions. A service should represent a meaningful domain area (e.g., “Payments,” not “ProcessCreditCard”).

3. Shared Database

Two or more services reading and writing to the same physical database schema.

• Symptoms: Fear of changing a table because you don’t know who else uses it; performance bottlenecks; constant schema locking.
• The Fix: Database Per Service. Each service owns its data. If Service A needs Service B’s data, it should request it via an API or subscribe to an event.

4. Over-Engineering (The “Netflix” Syndrome)

Implementing complex service meshes, circuit breakers, and global scale on Day 1 for a system that has 10 users.

• Symptoms: Developers spending more time on infrastructure than business features.
• The Fix: Follow YAGNI (You Ain’t Gonna Need It). Start with a simple “Modular Monolith” and only split when you have a clear reason (scaling, team size, independent deployment).

5. Ignoring Observability

Thinking you can debug a distributed system using console logs.

• Symptoms: “It works on my machine” but fails in production; not knowing where a request failed; high MTTR (Mean Time to Resolution).
• The Fix: Implement Distributed Tracing (OpenTelemetry) and Centralized Logging from the start.

graph TD
    User --> Gateway
    Gateway --> Svc1
    Svc1 --> Svc2
    Svc2 --> Svc3
    
    Svc1 -.-> Trace((Tracing Collector))
    Svc2 -.-> Trace
    Svc3 -.-> Trace
    Gateway -.-> Trace

6. The “God” Service

One microservice that does 80% of the work while the others are just tiny helpers. This “God Service” becomes the new bottleneck.

• The Fix: Re-evaluate your domain boundaries. Use Event Storming to identify where the logic actually belongs.

Summary Table: Anti-Pattern vs. Solution

Closing Thought

Microservices are about autonomy. If your architecture doesn’t allow teams to move independently, it’s not a microservices architecture — it’s just a more expensive monolith.

Ready to build something the right way? Revisit my post on Incremental Modernization.