What Technical Challenges Should You Consider Before Choosing Microservices?

Mar 28, 2026

Problem

I started a new project and immediately designed it as microservices. Six months later, our “microservices” architecture had become a distributed monolith—services that couldn’t deploy independently, debugging sessions that took days, and a production incident that nobody could trace.

The worst part? We only had three developers.

I assumed microservices were the “modern” way to build software. Spring Boot made it easy—just add @EnableDiscoveryClient, throw in some REST templates, and boom, microservices! But nobody told me about the operational complexity hidden behind those annotations.

The Trap

Here’s what I heard: “Microservices let you scale independently. Each team can own their service. Spring Boot makes it easy.”

Here’s what I experienced:

Month 1: Setup was smooth, Spring Boot handled everything
Month 2: Added inventory service, payment service, order service
Month 3: Debugging took 4x longer than monolith
Month 4: A network timeout caused a cascading failure
Month 5: We couldn't trace which service caused the bug
Month 6: We realized all services deploy together anyway

That Reddit discussion I found later captured it perfectly: “The project starts as a micro service but towards the end it becomes monolith.”

Challenge #1: Distributed Transactions

In a monolith, a transaction is easy:

@Transactional
public Order createOrder(OrderRequest request) {
    Inventory inventory = inventoryRepo.findById(request.itemId);
    if (inventory.getStock() < request.quantity) {
        throw new InsufficientStockException();
    }
    inventory.decreaseStock(request.quantity);
    Payment payment = paymentRepo.save(new Payment(request.amount));
    Order order = orderRepo.save(new Order(request, payment));
    return order;
}
// One transaction, all succeeds or all fails

In microservices, that same operation becomes a distributed system problem:

public Order createOrder(OrderRequest request) {
    // Step 1: Check inventory (remote call)
    InventoryResponse inventory = inventoryClient.checkStock(request.itemId);
    if (inventory.getStock() < request.quantity) {
        throw new InsufficientStockException();
    }

    // Step 2: Reserve inventory (remote call, might fail)
    inventoryClient.reserveStock(request.itemId, request.quantity);

    // Step 3: Process payment (remote call, might fail)
    PaymentResponse payment = paymentClient.processPayment(request.amount);

    // Step 4: Create order (local)
    Order order = orderRepo.save(new Order(request, payment));

    // What if payment succeeds but order save fails?
    // Need compensation logic: refund payment, release inventory
    // This is the Saga pattern - complex!
    return order;
}

I spent weeks implementing the Saga pattern. In a monolith, @Transactional handled everything. In microservices, I had to:

Design compensation transactions for every failure scenario
Handle partial failures gracefully
Implement idempotency for retries
Build a state machine for order status

The hidden cost: ACID transactions became MY problem, not the database’s problem.

Challenge #2: Network Reliability

I learned this the hard way: networks fail constantly in production.

Scenario 1: Inventory service timeout during Black Friday
  → Order service hung waiting
  → Users saw "processing" forever
  → Had to implement circuit breakers

Scenario 2: Payment service returned 500 but payment went through
  → User retried, got double-charged
  → Had to implement idempotency keys

Scenario 3: Service mesh configuration error
  → Requests routed to wrong version
  → Debugging took 6 hours across 3 services

The code that worked locally:

public Order createOrder(OrderRequest request) {
    InventoryResponse inventory = inventoryClient.checkStock(request.itemId);
    // This works fine locally, fails randomly in production
    return processOrder(inventory);
}

The code that survives production:

@CircuitBreaker(name = "inventory", fallbackMethod = "fallbackInventory")
@Retry(name = "inventory")
@Timeout(name = "inventory")
public Order createOrder(OrderRequest request) {
    try {
        InventoryResponse inventory = inventoryClient.checkStock(request.itemId);
        if (inventory == null) {
            throw new ServiceUnavailableException("Inventory service unavailable");
        }
        return processOrder(inventory);
    } catch (FeignException.Timeout e) {
        log.warn("Inventory service timeout, using fallback");
        return fallbackInventory(request, e);
    } catch (FeignException.InternalServerError e) {
        log.error("Inventory service error: {}", e.getMessage());
        throw new OrderProcessingException("Please retry later");
    }
}

Every remote call now needs: retries, timeouts, circuit breakers, fallbacks. That’s 4x more code per service call.

Challenge #3: Service Discovery

In a monolith, services find each other through function calls. In microservices, services need to find each other over the network.

spring:
  application:
    name: order-service
  cloud:
    kubernetes:
      discovery:
        enabled: true
eureka:
  client:
    service-url:
      defaultZone: http://eureka:8761/eureka/

This looks simple, but now I need:

A service registry (Eureka, Consul, or Kubernetes DNS)
Health checks for every service
Load balancing across instances
Configuration for all of the above

┌─────────────────────────────────────────────────────┐
│                   Service Registry                   │
│                   (Eureka/Consul)                   │
└─────────────────────────────────────────────────────┘
        ▲                    ▲                    ▲
        │ register           │ register          │ register
        │                    │                   │
   ┌────┴────┐         ┌────┴────┐        ┌────┴────┐
   │ Order   │         │Inventory│        │ Payment │
   │ Service │         │ Service │        │ Service │
   └─────────┘         └─────────┘        └─────────┘
        │                    │                    │
        │ discover           │ discover           │ discover
        │                    │                    │
        └────────────────────┴────────────────────┘
                    Network calls

When Eureka went down, our entire system became unreachable—not because services were down, but because they couldn’t find each other.

Challenge #4: Debugging and Tracing

This is where I lost the most time. A single user request in our system touched 5 services:

User Request → API Gateway → Order Service
                           → Inventory Service
                           → Payment Service
                           → Notification Service
                           → Analytics Service

When something went wrong, I had to:

Find the right logs across 5 services
Correlate logs by timestamp (hoping clocks were synced)
Trace the request path manually
Guess which service caused the issue

Without distributed tracing, debugging looked like this:

# SSH into order-service
grep "orderId=123" /var/log/order-service.log
# Found: "Order created successfully"

# SSH into inventory-service
grep "orderId=123" /var/log/inventory-service.log
# Found: "Stock reserved"

# SSH into payment-service
grep "orderId=123" /var/log/payment-service.log
# Found: "Payment processed"

# But notification-service?
grep "orderId=123" /var/log/notification-service.log
# Nothing! Did it fail? Did it not get called?

Distributed tracing (Zipkin, Jaeger) helps, but requires setup:

@Configuration
public class TracingConfig {
    @Bean
    public RestTemplate restTemplate() {
        RestTemplate template = new RestTemplate();
        // Interceptor to propagate trace headers
        template.getInterceptors().add(new TracingInterceptor());
        return template;
    }
}

Every service needs this. Every framework needs configuration. Every log needs to include trace IDs. In a monolith? Just grep the log file.

Challenge #5: Deployment and Operations

With a monolith, deployment was:

./mvnw package
java -jar app.jar
# Done

With microservices, deployment became:

1. Build order-service
2. Build inventory-service
3. Build payment-service
4. Build notification-service
5. Build API gateway
6. Push 5 Docker images
7. Update 5 Kubernetes deployments
8. Wait for all services to be healthy
9. Verify service mesh routing
10. Check distributed tracing is working
11. Monitor for 30 minutes
12. Rollback if any service fails

And version compatibility became a real concern:

┌──────────────────────────────────────────────────────────┐
│                 Order Service v2.1.0                      │
│  Requires: Inventory v1.3+, Payment v2.0+                 │
└──────────────────────────────────────────────────────────┘
                           │
           ┌───────────────┼───────────────┐
           ▼               ▼               ▼
   ┌───────────┐   ┌───────────┐   ┌───────────┐
   │Inventory  │   │ Payment   │   │ Notification│
   │ v1.3.2    │   │ v2.1.0    │   │ v1.0.0     │
   │ ✓ Works   │   │ ✓ Works   │   │ ✓ Works    │
   └───────────┘   └───────────┘   └───────────┘

But wait, Payment v2.1.0 has a bug...
Can we rollback Payment to v2.0.0?
Order v2.1.0 requires Payment v2.0+
Should work... or will it break something else?

Challenge #6: Testing Complexity

Testing microservices requires running multiple services:

Unit Test:           Just the service
Integration Test:    Service + Database + Cache
Contract Test:       Service + Mock dependencies
E2E Test:            All 5 services + database + message queue

I spent more time setting up test environments than writing tests:

@SpringBootTest
@ActiveProfiles("test")
@AutoConfigureMockMvc
class OrderServiceIntegrationTest {

    @MockBean
    private InventoryClient inventoryClient;

    @MockBean
    private PaymentClient paymentClient;

    @MockBean
    private NotificationClient notificationClient;

    // If I forget one mock, the test fails to start
    // If a mock behaves differently from real service, test passes but code fails in prod
}

In a monolith, integration tests just ran. In microservices, every test needs a network of mocks and stubs.

The Comparison

Aspect              Monolith          Microservices
─────────────────────────────────────────────────────────
Local Development   One command       Multiple services, infrastructure
Debugging           grep + logs       Distributed tracing setup
Testing             Run tests         Mock every dependency
Deployment          One artifact      Multiple coordinated deployments
Transaction         @Transactional    Saga pattern implementation
Network failures    N/A               Circuit breakers, retries, timeouts
Service discovery   Function calls    Registry, health checks, DNS
Team size needed    2-3 developers    10+ developers, DevOps team
Initial setup       1 day             1-2 weeks

Common Mistakes

Mistake 1: “Spring Boot Makes Microservices Easy”

Spring Boot makes the CODE easy. It doesn’t solve:

Network latency
Distributed transactions
Operational complexity
Debugging distributed systems

@EnableDiscoveryClient  // Doesn't solve service discovery outages
@EnableCircuitBreaker   // Doesn't solve the need for fallback logic
@FeignClient           // Doesn't solve network reliability
// These are tools, not solutions

Mistake 2: Creating a Distributed Monolith

We split our code into services but kept them tightly coupled:

- Every service change requires deploying other services
- Services share the same database
- One service failure brings down the system
- Can't deploy services independently

This is the worst of both worlds: monolith complexity AND microservices overhead.

Mistake 3: Underestimating Team Requirements

Microservices need:

DevOps expertise (not optional)
Dedicated infrastructure team
On-call rotation for multiple services
Monitoring and alerting setup

I tried to run microservices with 3 developers. We spent 60% of our time on infrastructure, 40% on features.

Mistake 4: Choosing Microservices Based on Team Size

The rule I heard was “one service per team of 6-8.” But the real rule is:

✓ 10+ developers on the project
✓ DevOps maturity (CI/CD, monitoring, alerting)
✓ Clear service boundaries (domain-driven design)
✓ Operational budget for infrastructure
✓ Clear scaling requirements (not just "might need to scale")

If you don’t have ALL of these, start with a monolith.

When to Choose Microservices

After this experience, I developed a simple checklist:

[ ] Multiple teams (10+ developers)
[ ] Clear domain boundaries (bounded contexts defined)
[ ] Proven scaling requirements (actual data, not assumptions)
[ ] DevOps team in place
[ ] Monitoring and tracing infrastructure ready
[ ] Budget for increased operational costs
[ ] Business can tolerate eventual consistency

If you can't check ALL boxes: start with a monolith.
You can always extract services later.

What I Do Now

I start every project as a well-structured monolith:

Modular architecture - Clean boundaries between modules
Interface-based design - Services can be extracted later
Event-driven communication - Internal events become external events
Domain-driven design - Bounded contexts become service boundaries

// Order module - internal but isolated
public class OrderService {
    private final OrderRepository orderRepo;
    private final InventoryService inventoryService; // Interface
    private final PaymentService paymentService;     // Interface
    // These are in-process now, can become remote later
}

// When extraction is needed:
// 1. Implement InventoryService as REST client
// 2. Deploy Inventory module separately
// 3. Update configuration
// No architecture rewrite needed

This approach gives me the option to extract services when I actually need them, not when I imagine I might.

Summary

Microservices introduce six major technical challenges:

Distributed transactions - No more @Transactional, implement Saga patterns
Network reliability - Retries, timeouts, circuit breakers everywhere
Service discovery - Infrastructure just to find services
Debugging - Distributed tracing, log correlation, no more grep
Deployment - Coordinated releases, version compatibility
Testing - Mock everything, integration tests need entire environment

These challenges are justified only for large teams with mature DevOps practices. Most projects should start as monoliths and evolve to microservices when specific needs emerge.

The Reddit discussion was right: “Don’t start with micro services too soon. It’s often not beneficial.”

I learned this the hard way. Don’t make my mistake.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit: Spring Boot for Monolith or Microservices?
👨‍💻 Martin Fowler: Microservices
👨‍💻 Spring Boot Documentation

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!