Spring Boot Microservices Architecture Patterns

Why practical patterns matter as systems grow beyond a single deployable.

I have shipped monoliths that were a joy to deploy and microservices that made on-call weeks feel longer than they should. Over the years, Spring Boot has become my default tool for building microservices in Java ecosystems. It is not the only way, but it often ends up being the most balanced approach for teams that value maintainability, clear contracts, and a mature runtime. This article walks through the architecture patterns I reach for in real projects, with examples grounded in Spring Boot, and shares tradeoffs you only notice after a few production cycles.

If you are deciding how to split a system, how to handle configuration at scale, or how to design reliable communication between services, this guide will meet you where you are. We will avoid abstract diagrams and focus on code and workflows that map directly to production concerns. Along the way, I will point out what Spring Boot does well, where it struggles, and where other choices may make more sense.

Where Spring Boot fits in modern microservices

Spring Boot sits at the center of a mature Java ecosystem that includes Spring Cloud, Spring Data, Spring Security, and an expansive set of tools for observability, messaging, and deployment. In real-world projects, teams use Spring Boot microservices to build domains that can be independently deployed, scaled, and tested. Typical users include backend engineers working on e-commerce platforms, fintech ledgers, logistics and inventory systems, healthcare data pipelines, and internal platform services.

Compared to alternatives, Spring Boot tends to offer a balanced profile:

• Versus Go or Rust services: Spring Boot provides higher developer productivity for business logic, especially with data access and declarative security, but it generally uses more memory and may have slower startup times. Go and Rust can be better for edge or ultra-low-latency components.
• Versus Node.js services: Spring Boot often provides stronger typing, transaction management, and integration with enterprise messaging. Node.js excels at I/O-heavy workloads with relatively simple domain logic and offers fast startup times for serverless.
• Versus Quarkus or Micronaut: Spring Boot remains the most widely adopted framework in Java microservices, with the largest ecosystem and community. Quarkus and Micronaut deliver faster startup and lower memory usage, especially for serverless or Kubernetes-native workloads. Spring Boot 3 with GraalVM native images narrows the gap, but it still requires careful library selection.

In practice, I choose Spring Boot when the team values a large ecosystem, predictable patterns, and maintainability. I choose Quarkus or Micronaut when startup time and memory footprint are critical, and I choose Go or Rust when raw performance or minimal runtime is a requirement.

Core patterns for Spring Boot microservices

API-first design and contracts

An API-first approach reduces coupling between services. OpenAPI (Swagger) is the standard I use most often. By starting with an OpenAPI spec, frontend and backend teams agree on contracts, and code generation reduces errors.

In a Maven-based project, I often generate interfaces and models from an OpenAPI spec using openapi-generator-maven-plugin. This keeps the HTTP layer in sync with the spec and makes breaking changes obvious during code review.

<plugin>
  <groupId>org.openapitools</groupId>
  <artifactId>openapi-generator-maven-plugin</artifactId>
  <version>7.6.0</version>
  <executions>
    <execution>
      <id>generate-api</id>
      <goals>
        <goal>generate</goal>
      </goals>
      <configuration>
        <inputSpec>${project.basedir}/src/main/resources/openapi/orders-api.yaml</inputSpec>
        <generatorName>spring</generatorName>
        <apiPackage>com.example.orders.api</apiPackage>
        <modelPackage>com.example.orders.model</modelPackage>
        <additionalProperties>
          <java8>true</java8>
          <dateLibrary>java8</dateLibrary>
          <useSpringBoot3>true</useSpringBoot3>
          <interfaceOnly>true</interfaceOnly>
        </additionalProperties>
        <configOptions>
          <useBeanValidation>true</useBeanValidation>
        </configOptions>
      </configuration>
    </execution>
  </executions>
</plugin>

A minimal OpenAPI spec (orders-api.yaml) might look like:

openapi: 3.0.3
info:
  title: Orders API
  version: 1.0.0
paths:
  /v1/orders:
    post:
      operationId: createOrder
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateOrderRequest'
      responses:
        '201':
          description: Order created
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/OrderResponse'
components:
  schemas:
    CreateOrderRequest:
      type: object
      required:
        - customerId
        - items
      properties:
        customerId:
          type: string
          format: uuid
        items:
          type: array
          items:
            $ref: '#/components/schemas/OrderItem'
    OrderItem:
      type: object
      required:
        - productId
        - quantity
      properties:
        productId:
          type: string
        quantity:
          type: integer
          format: int32
    OrderResponse:
      type: object
      properties:
        orderId:
          type: string
          format: uuid
        status:
          type: string
          enum: [PENDING, CONFIRMED, SHIPPED]

This approach pays off when multiple services call the Orders API. The generated interface ensures a single source of truth. If the contract changes, the build breaks in a predictable place.

Service decomposition and boundaries

Clear boundaries prevent microservices from becoming a distributed monolith. A useful rule of thumb is Domain-Driven Design’s Bounded Context. For an e-commerce domain, you might have:

• Orders service: Owns order creation, lifecycle, and status transitions.
• Payments service: Owns payment authorization and refunds.
• Inventory service: Owns stock reservations and adjustments.

In code, I make boundaries explicit by package-per-domain and avoiding direct database sharing. Inter-service communication happens via REST or messaging, not via shared tables.

An example OrdersController (generated by openapi-generator) can delegate to a domain service:

package com.example.orders.api;

import com.example.orders.model.CreateOrderRequest;
import com.example.orders.model.OrderResponse;
import com.example.orders.service.OrderService;
import jakarta.validation.Valid;
import org.springframework.http.ResponseEntity;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class OrdersApiController implements OrdersApi {

  private final OrderService orderService;

  public OrdersApiController(OrderService orderService) {
    this.orderService = orderService;
  }

  @Override
  public ResponseEntity<OrderResponse> createOrder(@Valid CreateOrderRequest createOrderRequest) {
    OrderResponse response = orderService.create(createOrderRequest);
    return ResponseEntity.status(201).body(response);
  }
}

The OrderService enforces business rules and coordinates with other services or messaging:

package com.example.orders.service;

import com.example.orders.model.CreateOrderRequest;
import com.example.orders.model.OrderResponse;
import org.springframework.stereotype.Service;
import org.springframework.transaction.annotation.Transactional;

@Service
public class OrderService {

  private final InventoryClient inventoryClient;
  private final PaymentClient paymentClient;
  private final OrderRepository orderRepository;

  public OrderService(InventoryClient inventoryClient,
                      PaymentClient paymentClient,
                      OrderRepository orderRepository) {
    this.inventoryClient = inventoryClient;
    this.paymentClient = paymentClient;
    this.orderRepository = orderRepository;
  }

  @Transactional
  public OrderResponse create(CreateOrderRequest request) {
    // Reserve inventory
    inventoryClient.reserve(request.getItems());

    // Create order in PENDING state
    var order = Order.from(request);
    order.setStatus("PENDING");
    order = orderRepository.save(order);

    // Initiate payment authorization
    paymentClient.authorize(order.getId(), request.getCustomerId());

    return OrderResponse.from(order);
  }
}

This is intentionally simple. Real systems add idempotency keys, saga orchestration, and compensation logic, which we will cover later.

Synchronous communication: REST and Feign

REST is often the first choice for service-to-service calls. Spring’s RestTemplate is fine, but I prefer Spring Cloud OpenFeign for its declarative style and built-in integration with Spring Cloud components like load balancing and circuit breakers.

Feign client example:

package com.example.orders.client;

import com.example.orders.model.PaymentAuthorizationRequest;
import org.springframework.cloud.openfeign.FeignClient;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;

@FeignClient(name = "payments-service", url = "${payments.service.url}")
public interface PaymentClient {

  @PostMapping("/v1/payments/authorize")
  void authorize(@RequestBody PaymentAuthorizationRequest request);
}

The client is used by the service layer as shown earlier. In a Kubernetes environment, you might omit the url property and rely on service discovery via Spring Cloud Kubernetes. For resilience, add a circuit breaker:

@FeignClient(name = "payments-service", configuration = FeignConfig.class)
public interface PaymentClient { ... }

@Configuration
public class FeignConfig {
  @Bean
  public Retryer retryer() {
    return new Retryer.Default(100, 1000, 3);
  }
}

And wrap calls with Resilience4j:

@Service
public class OrderService {

  private final PaymentClient paymentClient;
  private final CircuitBreaker circuitBreaker;

  public OrderService(PaymentClient paymentClient, CircuitBreakerRegistry registry) {
    this.paymentClient = paymentClient;
    this.circuitBreaker = registry.circuitBreaker("payments");
  }

  @Transactional
  public OrderResponse create(CreateOrderRequest request) {
    // ... reserve inventory ...

    var order = Order.from(request);
    order.setStatus("PENDING");
    order = orderRepository.save(order);

    // Try payment authorization with circuit breaker
    Try.run(() -> circuitBreaker.run(
      () -> paymentClient.authorize(new PaymentAuthorizationRequest(order.getId(), request.getCustomerId())),
      throwable -> fallbackAuthorize(order.getId())
    )).recover(throwable -> fallbackAuthorize(order.getId())).get();

    return OrderResponse.from(order);
  }

  private Void fallbackAuthorize(UUID orderId) {
    // Mark order for manual review or queue for retry
    // Alternatively, emit a domain event for async retry
    return null;
  }
}

This pattern helps avoid cascading failures and keeps the system stable when dependencies are slow.

Asynchronous communication: events and messaging

For eventual consistency across service boundaries, messaging is essential. Spring Cloud Stream with Kafka or RabbitMQ is widely used. A common pattern is publish-subscribe for domain events, such as OrderCreated and PaymentAuthorized.

A simple event publisher:

package com.example.orders.events;

import org.springframework.cloud.stream.function.StreamBridge;
import org.springframework.stereotype.Service;

@Service
public class OrderEventPublisher {

  private final StreamBridge streamBridge;

  public OrderEventPublisher(StreamBridge streamBridge) {
    this.streamBridge = streamBridge;
  }

  public void orderCreated(UUID orderId, UUID customerId) {
    var event = new OrderCreated(orderId, customerId, System.currentTimeMillis());
    streamBridge.send("orderCreated-out-0", event);
  }
}

A consumer in the Payments service:

package com.example.payments.handlers;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import java.util.function.Consumer;

@Configuration
public class PaymentEvents {

  @Bean
  public Consumer<OrderCreated> handleOrderCreated() {
    return event -> {
      // Authorize payment asynchronously
      // Store record and trigger external gateway call
    };
  }
}

Configuration in application.yml for Spring Cloud Stream and Kafka:

spring:
  cloud:
    stream:
      kafka:
        binder:
          brokers: localhost:9092
      bindings:
        orderCreated-out-0:
          destination: orders.events
          content-type: application/json
        handleOrderCreated-in-0:
          destination: orders.events
          content-type: application/json

This approach decouples services and handles spikes gracefully. However, it introduces complexity in ordering, deduplication, and replay. I recommend idempotency keys and consumer state stores to handle message redelivery.

Saga pattern for distributed transactions

For multi-step operations spanning services, the saga pattern coordinates partial transactions and compensations. Two common implementations: choreography (events only) and orchestration (a central coordinator). In Spring Boot, I often use orchestration for clarity.

A minimal orchestration service could be built as a Spring component that tracks state and emits commands:

package com.example.saga;

import org.springframework.stereotype.Service;
import java.util.UUID;

@Service
public class OrderCreationSaga {

  private final InventoryClient inventoryClient;
  private final PaymentClient paymentClient;

  public OrderCreationSaga(InventoryClient inventoryClient, PaymentClient paymentClient) {
    this.inventoryClient = inventoryClient;
    this.paymentClient = paymentClient;
  }

  public void start(UUID orderId, UUID customerId) {
    try {
      inventoryClient.reserve(orderId);
      paymentClient.authorize(orderId, customerId);
      // On success, emit OrderConfirmed event
    } catch (Exception e) {
      // Compensate: release inventory, cancel payment authorization
      inventoryClient.release(orderId);
      paymentClient.cancel(orderId);
    }
  }
}

For production, you might use an orchestration library like Temporal (Java SDK) or build a state machine with Spring StateMachine, persisting state in a relational store. The key is compensations that are idempotent and well-tested.

API gateway and edge concerns

Spring Cloud Gateway is a powerful reverse proxy for routing, filtering, and cross-cutting concerns. It can centralize authentication, rate limiting, and request transformation.

A typical gateway configuration routes by path and adds simple filters:

server:
  port: 8080
spring:
  cloud:
    gateway:
      routes:
        - id: orders
          uri: http://orders-service:8081
          predicates:
            - Path=/api/orders/**
          filters:
            - StripPrefix=1
        - id: payments
          uri: http://payments-service:8082
          predicates:
            - Path=/api/payments/**
          filters:
            - StripPrefix=1

For authentication, I often route to a dedicated auth service and inject JWT claims as headers downstream. This keeps services simple and avoids repeating auth logic.

Configuration and secrets management

Externalized configuration is critical. Spring Boot’s config server centralizes property management. For secrets, integrate with Vault or Kubernetes secrets.

A minimal config server:

package com.example.config;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.cloud.config.server.EnableConfigServer;

@SpringBootApplication
@EnableConfigServer
public class ConfigServerApplication {
  public static void main(String[] args) {
    SpringApplication.run(ConfigServerApplication.class, args);
  }
}

application.yml for config server:

server:
  port: 8888
spring:
  cloud:
    config:
      server:
        git:
          uri: https://github.com/example/config-repo
          default-label: main
          search-paths: '{application}'

Services fetch configuration via spring.config.import:

spring:
  config:
    import: configserver:http://localhost:8888

For secrets in Kubernetes, mount them as environment variables or use Spring Cloud Kubernetes with Vault integration. Avoid committing secrets to Git, even if encrypted. Manage lifecycle and rotation with a dedicated secrets platform.

Observability: logs, metrics, traces

Observability is where microservices succeed or fail. Spring Boot Actuator exposes health and metrics endpoints. Micrometer integrates with Prometheus and Grafana for metrics. OpenTelemetry provides distributed tracing.

A minimal Actuator setup:

<dependency>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
  <groupId>io.micrometer</groupId>
  <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>

application.yml:

management:
  endpoints:
    web:
      exposure:
        include: health,info,metrics,prometheus
  metrics:
    export:
      prometheus:
        enabled: true

For tracing with OpenTelemetry, add the OTel agent or use the Spring Boot starter. Configure the exporter:

otel:
  exporter:
    otlp:
      endpoint: http://otel-collector:4317
  service:
    name: orders-service

In Grafana, build dashboards around RED metrics (rate, errors, duration) and link traces to logs using correlation IDs. This is where many teams underestimate the operational value. Start with consistent request IDs propagated across services.

Resilience and fault tolerance

Resilience patterns are crucial when services evolve independently. Circuit breakers, retries with backoff, bulkheads, and timeouts are baseline requirements.

Resilience4j integration example:

@Bean
public CircuitBreakerRegistry circuitBreakerRegistry() {
  CircuitBreakerConfig config = CircuitBreakerConfig.custom()
    .failureRateThreshold(50)
    .waitDurationInOpenState(Duration.ofSeconds(30))
    .permittedNumberOfCallsInHalfOpenState(10)
    .slidingWindowType(CircuitBreakerConfig.SlidingWindowType.COUNT_BASED)
    .slidingWindowSize(20)
    .build();
  return CircuitBreakerRegistry.of(config);
}

Combine this with Spring Retry in Feign clients for transient errors, but avoid infinite retries. Use exponential backoff and jitter. For message consumers, limit concurrency and add dead-letter queues for poison messages.

Security patterns

Security spans authentication, authorization, and secure transport. Spring Security makes OAuth2 and JWT integration straightforward. For service-to-service, I use mTLS or short-lived tokens via a central auth provider.

A simple resource server configuration:

package com.example.security;

import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.security.config.annotation.web.builders.HttpSecurity;
import org.springframework.security.web.SecurityFilterChain;

@Configuration
public class SecurityConfig {

  @Bean
  public SecurityFilterChain filterChain(HttpSecurity http) throws Exception {
    http
      .authorizeHttpRequests(auth -> auth
        .requestMatchers("/actuator/health").permitAll()
        .anyRequest().authenticated()
      )
      .oauth2ResourceServer(oauth2 -> oauth2.jwt());
    return http.build();
  }
}

For gateway-level auth, validate JWTs and forward claims as headers. Always enforce HTTPS at the edge and use mTLS between internal services when sensitive data is involved.

Deployment patterns: containers and Kubernetes

Packaging Spring Boot services as container images is standard. Use a multistage Dockerfile to build a JRE-based image, then consider GraalVM native images for faster startup when the classpath is compatible.

A typical Dockerfile for a JVM image:

FROM eclipse-temurin:17-jre-jammy AS builder
WORKDIR /app
COPY target/orders-service.jar app.jar

FROM eclipse-temurin:17-jre-jammy
WORKDIR /app
COPY --from=builder /app/app.jar .
ENTRYPOINT ["java", "-jar", "app.jar"]

For native images, use the GraalVM builder and Spring Boot 3’s native support, but be mindful that some libraries (like Spring AOP and certain proxies) require configuration or may not be fully compatible.

Kubernetes manifests typically include ConfigMaps, Secrets, and Horizontal Pod Autoscalers. Health probes should be wired to Actuator endpoints:

apiVersion: v1
kind: ConfigMap
metadata:
  name: orders-config
data:
  APPLICATION_YAML: |
    server:
      port: 8080
    management:
      endpoints:
        web:
          exposure:
            include: health,info
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: orders-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: orders
  template:
    metadata:
      labels:
        app: orders
    spec:
      containers:
      - name: orders
        image: orders-service:latest
        ports:
        - containerPort: 8080
        envFrom:
        - configMapRef:
            name: orders-config
        livenessProbe:
          httpGet:
            path: /actuator/health/liveness
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /actuator/health/readiness
            port: 8080
          initialDelaySeconds: 10
          periodSeconds: 5

This setup enables safe rolling updates and fast detection of unhealthy pods.

Project structure and workflow

A clean project layout reduces cognitive load. Here is a typical Maven layout for a service:

orders-service/
├── pom.xml
├── src/
│   ├── main/
│   │   ├── java/
│   │   │   └── com/example/orders/
│   │   │       ├── api/           # Generated or hand-written controllers
│   │   │       ├── service/       # Domain logic
│   │   │       ├── repository/    # Spring Data repositories
│   │   │       ├── client/        # Feign clients
│   │   │       ├── events/        # Messaging publishers/consumers
│   │   │       ├── model/         # Domain models and DTOs
│   │   │       ├── config/        # Beans, security, resilience
│   │   │       └── OrdersApplication.java
│   │   └── resources/
│   │       ├── application.yml    # Environment-specific properties
│   │       ├── openapi/           # OpenAPI specs
│   │       └── logback-spring.xml # Logging configuration
│   └── test/
│       ├── java/
│       │   └── com/example/orders/
│       │       ├── service/       # Unit tests
│       │       └── api/           # MVC tests
│       └── resources/
│           └── application-test.yml
├── Dockerfile
└── README.md

For multi-service development, I keep a separate compose setup in a top-level devops directory. A docker-compose file spins up Kafka, Postgres, Redis, and the gateway. This local environment mirrors production, which improves feedback loops.

Strengths and tradeoffs

Strengths

• Ecosystem maturity: Spring Boot integrates with almost every enterprise system. Security, data, messaging, and observability are well supported.
• Developer productivity: Spring Boot’s conventions reduce boilerplate. Features like Spring Data, Actuator, and Spring Cloud Stream accelerate development.
• Maintainability: Patterns like Feign, Gateway, and Config Server provide standard approaches that new team members can learn quickly.
• Strong typing and tooling: Java’s type system and IDE support help catch errors early. Refactoring is safer than in dynamic languages.

Weaknesses

• Resource usage: Spring Boot services consume more memory than Go or Rust equivalents. Startup time can be slower, especially without native images.
• Complexity at scale: Microservices introduce distributed systems complexity. Without strong discipline, you end up with a distributed monolith.
• Version compatibility: Upgrading Spring Boot and Spring Cloud requires care, especially with third-party libraries and OpenTelemetry agents.

When to choose Spring Boot microservices

• Choose Spring Boot when your team is comfortable in the JVM ecosystem, you need rich integrations, and maintainability is a priority.
• Choose Quarkus or Micronaut for serverless or constrained environments where fast startup and low memory are critical.
• Choose Go or Rust for services that require minimal resource footprints or ultra-low latency at the edge.

I have seen teams succeed with Spring Boot microservices when they invest early in observability and a strong deployment pipeline. Conversely, teams without these practices often struggle, regardless of framework choice.

Personal experience: lessons from the trenches

One project involved splitting a monolith into Orders, Payments, and Inventory services. We started with synchronous REST calls and quickly learned that retries without idempotency keys led to duplicate inventory reservations. The fix was a combination of idempotent inventory endpoints, a correlation ID header, and a simple saga using events. That experience made me default to asynchronous event-driven patterns for cross-domain updates and reserve synchronous calls for read-heavy or low-latency paths.

Another learning moment came when we migrated to Kubernetes. Liveness and readiness probes initially pointed to the same Actuator endpoint, causing restart cascades during high load. After splitting liveness and readiness and adding a dedicated downstream health check for the database, deployments stabilized. Since then, I always model health as three layers: app health, dependency health, and business health.

Debugging distributed systems is where OpenTelemetry proved invaluable. Having end-to-end traces made it obvious that a single slow database query in the Orders service was causing timeouts upstream in the API gateway. This led to a query plan change and an index addition that improved p95 latency by 40 percent. Tracing turned hours of guesswork into minutes of focused analysis.

Getting started: setup and mental models

• Start with a clear domain model. Sketch bounded contexts before writing code. Identify which service owns which data and how updates flow.
• Define API contracts early. Use OpenAPI and code generation to keep frontend and backend aligned.
• Use a consistent configuration strategy. Centralize configuration with Spring Cloud Config and manage secrets outside of Git.
• Build a local platform. Use docker-compose for Kafka, Postgres, and the gateway. Iterate fast before moving to Kubernetes.
• Add observability from day one. Actuator, Prometheus metrics, and structured logging will save you from painful debugging later.
• Plan for resilience. Use circuit breakers for external calls, idempotent message handlers, and retries with backoff.
• Automate deployments. Create CI pipelines that run tests, build container images, and push to a registry. Use Kubernetes manifests or Helm charts for consistent deployments.

Tooling that helps:

• IntelliJ IDEA with Spring Boot and OpenAPI plugins
• Maven or Gradle (choose one and stick to it across services)
• Docker and docker-compose for local development
• Kubernetes for staging and production
• Prometheus and Grafana for metrics
• OpenTelemetry for tracing
• Vault or Kubernetes secrets for secrets management

Free learning resources

• Spring Boot Reference Documentation: https://spring.io/projects/spring-boot
• Spring Cloud Documentation: https://spring.io/projects/spring-cloud
• Spring Cloud Gateway: https://spring.io/projects/spring-cloud-gateway
• Spring Cloud Stream: https://spring.io/projects/spring-cloud-stream
• Resilience4j Documentation: https://resilience4j.readthedocs.io
• OpenTelemetry Java Documentation: https://opentelemetry.io/docs/languages/java/
• Prometheus Documentation: https://prometheus.io/docs/
• Grafana Documentation: https://grafana.com/docs/
• Domain-Driven Design (Eric Evans): https://martinfowler.com/bliki/DomainDrivenDesign.html
• Microservices Patterns (Chris Richardson): https://microservices.io/

These resources are valuable because they provide both theoretical grounding and practical examples. I often revisit the Microservices Patterns site when designing sagas or API gateways.

Summary: who should use Spring Boot microservices and who might skip it

If you are building a distributed system with multiple domains, need mature integration with security and messaging, and value maintainability over raw performance, Spring Boot microservices are an excellent choice. The ecosystem reduces decision fatigue and helps teams standardize on patterns for configuration, resilience, and observability. For teams already familiar with Java and Spring, the learning curve is manageable, and the long-term payoff in maintainability is real.

If your workload is highly resource-constrained, requires sub-second startup in serverless contexts, or you need the absolute lowest latency, consider Quarkus, Micronaut, Go, or Rust. If your team is small and your domain is simple, a well-structured monolith with clear modules might be a better fit than microservices. Microservices add operational complexity; they should solve a problem, not create one.

The best architecture is the one your team can operate confidently. Spring Boot gives you the tools to build, observe, and evolve microservices, but the practices you adopt around boundaries, contracts, and observability determine success more than the framework alone.