How Does Spring Boot Deployment Differ Between Monoliths and Microservices?

Mar 28, 2026

Purpose

This post clarifies whether Spring Boot applications deploy differently when built as a monolith versus as microservices.

The Problem

When I planned to transition my team from a monolithic architecture to microservices, I got this question from management:

Will our deployment pipeline need a complete overhaul for microservices?
Do we need new tools, new processes, new infrastructure?

I spent weeks researching this, expecting to find dramatically different deployment strategies. But what I found surprised me.

What I Discovered

I started by comparing our current monolith deployment with how we’d deploy microservices:

Build (mvn package) -> Docker Image -> Kubernetes -> Done

Build (???) -> Special Packaging (???) -> Service Mesh (???) -> API Gateway (???) -> Kubernetes (???) -> Done?

I assumed microservices required some special Spring Boot configuration or deployment magic. They don’t.

Here’s what I found from actual deployment manifests:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: monolith-app
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: app
        image: myorg/monolith:latest
        ports:
        - containerPort: 8080

# Identical pattern - just multiple deployments
apiVersion: apps/v1
kind: Deployment
metadata:
  name: user-service
spec:
  replicas: 2
  template:
    spec:
      containers:
      - name: user-service
        image: myorg/user-service:latest
        ports:
        - containerPort: 8080
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: order-service
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: order-service
        image: myorg/order-service:latest
        ports:
        - containerPort: 8080

The deployment YAML is nearly identical. The only difference: I deploy multiple services instead of one.

The Build Process: Same for Both

I tested this by building both architectures:

mvn clean package
# Produces: target/myapp.jar

mvn clean package
# Produces: target/user-service.jar

Same command. Same output format. Same executable JAR with embedded Tomcat.

The Dockerfile: Identical

Here’s the Dockerfile I use for both:

FROM eclipse-temurin:17-jre-alpine
WORKDIR /app
COPY target/myapp.jar app.jar
EXPOSE 8080
ENTRYPOINT ["java", "-jar", "app.jar"]

Whether I’m containerizing a monolith or a microservice, this Dockerfile works unchanged. Spring Boot’s executable JAR format makes both architectures deploy identically.

The Real Differences

After deploying both architectures, I identified where the differences actually lie:

1. Communication Pattern

Service A --[method call]--> Service B
- In-memory, nanoseconds
- No network latency
- No serialization overhead

Service A --[HTTP/gRPC]--> Service B
- Network call, milliseconds
- Network latency
- Serialization/deserialization
- Need service discovery

This is where I needed new infrastructure: service discovery (Consul/Eureka), API gateways (Spring Cloud Gateway), and circuit breakers (Resilience4j).

2. Database Strategy

# Single database for everything
spring.datasource.url=jdbc:postgresql://db:5432/appdb

# Each service has its own database
spring.datasource.url=jdbc:postgresql://user-db:5432/userdb
spring.application.name=user-service

The monolith uses one database. Each microservice typically owns its data.

3. Operational Complexity

- 1 deployment to monitor
- 1 log stream to analyze
- 1 set of metrics
- Simple debugging (single process)

- N deployments to monitor
- N log streams (need centralized logging)
- N sets of metrics (need distributed tracing)
- Complex debugging (distributed transactions)

This is the real cost of microservices—not deployment, but operations.

What I Got Wrong Initially

I made several mistakes when transitioning:

Mistake 1: Over-engineering the Deployment Pipeline

I spent two weeks designing a “special” microservices deployment pipeline:

Original Plan:
- Custom build tooling per service
- Service-specific Docker base images
- Complex deployment orchestration

Turns out, I could reuse the exact same pipeline:

stages:
  - build
  - docker
  - deploy

build:
  script: mvn clean package
  artifacts:
    paths:
      - target/*.jar

docker:
  script: docker build -t myorg/$SERVICE_NAME .

deploy:
  script: kubectl apply -f k8s/

The $SERVICE_NAME variable is the only thing that changes.

Mistake 2: Assuming Spring Boot Needed Special Configuration

I added unnecessary Spring Cloud dependencies thinking they were required for deployment:

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-kubernetes</artifactId>
</dependency>

These are for service discovery and config management, not deployment. A basic Spring Boot app deploys to Kubernetes without them.

Mistake 3: Creating Service-Specific Dockerfiles

I initially created different Dockerfiles per service:

# Dockerfile-user-service
FROM eclipse-temurin:17-jre-alpine
COPY target/user-service.jar app.jar

# Dockerfile-order-service
FROM eclipse-temurin:17-jre-alpine
COPY target/order-service.jar app.jar

One parametrized Dockerfile works for all services:

ARG SERVICE_NAME
FROM eclipse-temurin:17-jre-alpine
COPY target/${SERVICE_NAME}.jar app.jar
EXPOSE 8080
ENTRYPOINT ["java", "-jar", "app.jar"]

Why This Matters for Architecture Decisions

Understanding that deployment is identical changed how I evaluate monolith vs microservices:

Factor	Monolith	Microservices
Deployment complexity	Simple	Simple (same tools)
Operational complexity	Low	High
Development coordination	Higher	Lower
Data consistency	Easy	Hard (distributed transactions)
Scaling	Entire app	Per-service

The decision should be based on:

Team structure (Conway’s Law)
Domain complexity (bounded contexts)
Scaling requirements (independent scaling needs)
Operational maturity (can you handle distributed systems?)

Not deployment tooling—that’s the same for both.

What Changed in Our Transition

Our transition from monolith to microservices changed these things:

Number of deployments: From 1 to 10+ services
Inter-service communication: From method calls to REST/gRPC
Data ownership: From shared database to database-per-service
Observability stack: Added Jaeger for tracing, ELK for centralized logging
Testing complexity: Added contract testing, end-to-end tests

Our CI/CD pipeline stayed the same.

Summary

In this post, I showed that Spring Boot deployment is identical for monoliths and microservices. Both use the same build process (mvn package), the same containerization (Docker), and the same orchestration (Kubernetes).

The architectural differences are in communication patterns, data strategy, and operational complexity—not in how you deploy. When choosing between monolith and microservices, focus on team structure, domain boundaries, and scaling needs. Don’t let deployment concerns influence the decision—they’re the same for both.

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:

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