Quarkus vs Spring Boot for Microservices: Which Framework Should You Choose?
Problem
I was building a new microservices system and faced a framework decision. I went with Spring Boot because I’d used it for years. Six months later, my cloud bill was 3x what I budgeted, and my services took 8 seconds to start during auto-scaling events.
The real question I should have asked was: “Which framework matches my microservices deployment requirements?”
Not “What am I familiar with?” But “What problem am I solving?”
Why This Decision Matters More for Microservices
Java has always had a reputation: slow startup, high memory usage. For monoliths running 24/7, who cares? They start once and run forever.
But microservices are different:
┌─────────────────────────────────────────────────────────────┐│ Traditional Monolith Microservices │├─────────────────────────────────────────────────────────────┤│ Start once, run forever Scale up/down constantly ││ 2GB RAM? No problem 20 services x 2GB = 40GB ││ 10s startup? Whatever 10s x 20 pods = angry users ││ Single deploy 50 deploys/day │└─────────────────────────────────────────────────────────────┘When you have 20 microservices that need to scale up during peak hours, every second of startup time and every megabyte of memory matters.
My Failed Assumption: Spring Boot for Everything
I was a Spring Boot veteran. I knew Spring Security, Spring Data, Spring Cloud - the whole ecosystem. So naturally, I built all my microservices with Spring Boot.
Here’s what I deployed:
┌────────────────────────────────────────────────────────────┐│ Kubernetes Cluster │├────────────────────────────────────────────────────────────┤│ user-service (Spring Boot) → 300MB RSS, 8s start ││ order-service (Spring Boot) → 280MB RSS, 7s start ││ payment-service (Spring Boot) → 320MB RSS, 9s start ││ inventory-service (Spring Boot) → 290MB RSS, 7s start ││ notification-service (Spring Boot) → 250MB RSS, 6s start ││ analytics-service (Spring Boot) → 350MB RSS, 10s start│└────────────────────────────────────────────────────────────┘
Total Memory: ~1.8GBAverage Startup: 7-8 secondsCold start impact: Users experienced timeouts during auto-scalingThe numbers don’t lie. I was paying for memory I didn’t need and waiting for startups that hurt user experience.
The Performance Reality Check
After my cloud bill shock, I ran benchmarks:
Framework Startup Time Memory (RSS) Native Image Ready────────────────────────────────────────────────────────────────────Spring Boot (JVM) 2-10 seconds 200-350MB Yes (complex)Quarkus (JVM) 0.5-2 seconds 80-150MB Yes (simple)
Performance Comparison: Native Mode────────────────────────────────────────────────────────────────────Spring Boot Native 0.1-0.5 seconds 50-100MB Built-inQuarkus Native 0.02-0.05 seconds 12-30MB Built-inThe difference is dramatic. Quarkus native compiles to a tiny executable that starts in milliseconds.
What Makes Quarkus Different
Quarkus was designed specifically for cloud-native environments. Here’s why:
1. Build-Time Processing
// Quarkus does heavy lifting at BUILD time, not runtime@Path("/api/users")public class UserResource {
@Inject UserService userService;
@GET @Produces(MediaType.APPLICATION_JSON) public List<User> list() { return userService.findAll(); }}Traditional frameworks do annotation scanning, reflection, and classpath scanning at startup. Quarkus does all this during build, producing optimized bytecode.
2. Native Compilation Made Simple
# Quarkus (straightforward)./mvnw package -Pnative./target/my-app-1.0-runner
# Spring Boot (requires setup)# 1. Install GraalVM# 2. Configure native-maven-plugin# 3. Handle reflection hints manually# 4. Debug classpath issues./mvnw -Pnative native:compile./target/my-appQuarkus handles GraalVM complexity automatically. Spring Boot requires manual configuration.
3. Developer Experience
Both frameworks offer hot reload, but Quarkus’s dev mode is notably faster:
# Quarkus: Sub-second restartsmvn quarkus:dev
# Spring Boot: 2-5 second restartsmvn spring-boot:runWhen I Use Each Framework
After my costly lesson, I now have a clear decision framework:
Choose Quarkus When:
✓ Serverless (AWS Lambda, Azure Functions)✓ Kubernetes with frequent scaling✓ Microservices with strict cold-start requirements✓ Resource-constrained environments✓ Greenfield projects (no Spring dependencies)✓ Cost-sensitive deploymentsReal example: My notification service runs on AWS Lambda. With Spring Boot, cold starts were 8-10 seconds. With Quarkus native: 50ms.
@Named("notification")public class NotificationLambda implements RequestHandler<NotificationEvent, String> {
@Inject NotificationService service;
@Override public String handleRequest(NotificationEvent event, Context context) { return service.process(event); }}Choose Spring Boot When:
✓ Existing Spring ecosystem investments✓ Team with deep Spring expertise✓ Complex enterprise requirements✓ Mature tooling needs (Spring Security, Spring Cloud)✓ Services that run continuously (no cold starts)✓ Integration with legacy Spring systemsReal example: My payment service integrates with complex banking systems. Spring’s mature ecosystem and extensive documentation saved weeks of development.
@RestController@RequestMapping("/api/payments")public class PaymentController {
private final PaymentService paymentService; private final PaymentAuditService auditService; private final FraudDetectionService fraudService;
// Spring's dependency injection and transaction management // are battle-tested for enterprise scenarios @PostMapping @Transactional public ResponseEntity<Payment> processPayment( @Valid @RequestBody PaymentRequest request) { auditService.log(request); fraudService.check(request); return ResponseEntity.ok(paymentService.process(request)); }}The Hybrid Approach I Now Use
I don’t choose one framework for everything. I match frameworks to service requirements:
┌─────────────────────────────────────────────────────────────┐│ High-Scale, Event-Driven Services │├─────────────────────────────────────────────────────────────┤│ notification-service (Quarkus Native) ││ event-processor (Quarkus Native) ││ metrics-collector (Quarkus Native) ││ → Why: Frequent scaling, low memory, fast cold start │└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐│ Business-Critical, Complex Services │├─────────────────────────────────────────────────────────────┤│ payment-service (Spring Boot) ││ user-service (Spring Boot) ││ order-service (Spring Boot) ││ → Why: Complex business logic, mature ecosystem needed │└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐│ Background Processing Services │├─────────────────────────────────────────────────────────────┤│ report-generator (Spring Boot) ││ data-sync (Spring Boot) ││ → Why: Long-running, no cold start concerns │└─────────────────────────────────────────────────────────────┘Detailed Comparison
Ecosystem Maturity
Feature Spring Boot Quarkus─────────────────────────────────────────────────Security ★★★★★ ★★★★☆Data Access ★★★★★ ★★★★☆Cloud Integration ★★★★★ ★★★★☆Documentation ★★★★★ ★★★☆☆Community Size ★★★★★ ★★★☆☆Native Image Support ★★★☆☆ ★★★★★Startup Performance ★★☆☆☆ ★★★★★Memory Efficiency ★★☆☆☆ ★★★★★Learning Curve
Metric Spring Boot Quarkus─────────────────────────────────────────────────Documentation Depth Extensive GrowingTutorial Quality Excellent GoodStack Overflow Answers Millions ThousandsEnterprise Patterns Many Examples Fewer ExamplesTime to Productive 1-2 weeks 2-4 weeks (if Spring background)Code Comparison: Same Service, Different Frameworks
Let me show you the same microservice in both frameworks:
Quarkus Version
@Path("/api/users")@Produces(MediaType.APPLICATION_JSON)@Consumes(MediaType.APPLICATION_JSON)public class UserResource {
@Inject UserRepository repository;
@GET public List<User> list() { return repository.listAll(); }
@GET @Path("/{id}") public User get(@PathParam("id") Long id) { return repository.findByIdOptional(id) .orElseThrow(() -> new NotFoundException()); }
@POST @Transactional public Response create(User user) { repository.persist(user); return Response.created( URI.create("/api/users/" + user.id) ).build(); }}Spring Boot Version
@RestController@RequestMapping("/api/users")public class UserController {
private final UserRepository repository;
public UserController(UserRepository repository) { this.repository = repository; }
@GetMapping public List<User> list() { return repository.findAll(); }
@GetMapping("/{id}") public User get(@PathVariable Long id) { return repository.findById(id) .orElseThrow(() -> new ResponseStatusException( HttpStatus.NOT_FOUND )); }
@PostMapping public ResponseEntity<User> create(@RequestBody User user) { User saved = repository.save(user); return ResponseEntity .created(URI.create("/api/users/" + saved.getId())) .body(saved); }}Both are clean and readable. The difference isn’t in the code - it’s in the runtime behavior.
Performance in Production
After refactoring my architecture, here are the real numbers:
Service Memory Startup Monthly Cost────────────────────────────────────────────────────────user-service 300MB 8s $45order-service 280MB 7s $42payment-service 320MB 9s $48notification-service 250MB 6s $38event-processor 270MB 7s $40────────────────────────────────────────────────────────Total $213/month
After: Hybrid Approach────────────────────────────────────────────────────────user-service (SB) 300MB 8s $45order-service (SB) 280MB 7s $42payment-service (SB) 320MB 9s $48notification-service 25MB 0.05s $12 (Quarkus Native)event-processor 20MB 0.03s $10 (Quarkus Native)────────────────────────────────────────────────────────Total $157/month
Savings: $56/month (26% reduction)Cold start improvement: 99% faster for native servicesCommon Mistakes I Made
Mistake 1: Choosing Based on Familiarity Only
What I did: Used Spring Boot because I knew it well. What I should have done: Evaluated deployment requirements first. Serverless and frequent scaling favor Quarkus.
Mistake 2: Ignoring Memory Costs
What I did: Focused on development speed, not operational costs. What I should have done: Calculated cloud costs per service. 300MB vs 30MB adds up fast.
Mistake 3: Not Testing Native Compilation Early
What I did: Planned to “add native later”. What I should have done: Tested native builds from day one. Some libraries don’t work with native compilation.
Mistake 4: Over-Optimizing Everything
What I did: Considered rewriting everything in Quarkus. What I should have done: Focused on services that benefit most. Payment service doesn’t need 50ms startup - it runs 24/7.
Mistake 5: Assuming Spring Boot Can’t Do Native
Reality: Spring Boot supports GraalVM native images, but it requires more configuration:
<dependency> <groupId>org.springframework.experimental</groupId> <artifactId>spring-native</artifactId></dependency>
<plugin> <groupId>org.graalvm.buildtools</groupId> <artifactId>native-maven-plugin</artifactId></plugin>Spring Native works, but expect more configuration headaches than Quarkus.
Decision Framework
┌─────────────────────────────┐ │ New Microservice Decision │ └──────────────┬──────────────┘ │ ┌───────────────▼───────────────┐ │ Serverless or frequent │ │ auto-scaling required? │ └───────────────┬───────────────┘ ┌────────┴────────┐ YES NO │ │ ┌────────────▼───────┐ ┌──────▼───────────────┐ │ QUARKUS NATIVE │ │ Existing Spring │ │ (50ms cold start)│ │ ecosystem? │ │ (12-30MB memory) │ └──────┬───────────────┘ └───────────────────┘ ┌┴┐ │ YES │ NO ┌─────┴─────┐ │ │ ┌─────────▼───┐ ┌────▼────────────┐ │ SPRING BOOT │ │ Either works │ │ (leverage │ │ Consider team │ │ expertise) │ │ expertise │ └─────────────┘ └─────────────────┘Migration Strategy: What Worked for Me
If you’re considering adding Quarkus to your stack:
Phase 1: New Services First
Start new microservices with Quarkus. No legacy code to worry about.
Phase 2: Event-Driven Services
Services that respond to events (notifications, webhooks) benefit most from fast cold starts.
Phase 3: High-Scale Services
Services that scale frequently see the biggest cost savings.
Phase 4: Keep Spring Boot for Complex Services
Don’t rewrite services with complex business logic. Spring’s ecosystem pays off there.
High Priority (Quarkus):├── Event processors├── Notification services├── API gateways├── Health check services└── Scheduled task runners
Keep as Spring Boot:├── Payment processing├── User management├── Complex business logic└── Services with Spring dependenciesSummary
I shared my experience of using Spring Boot for all microservices and paying the price in cloud costs and cold start latency. The framework choice depends on your deployment requirements:
Choose Quarkus for microservices when:
- Serverless deployment (Lambda, Functions)
- Frequent auto-scaling in Kubernetes
- Memory costs matter
- Cold start latency matters
Choose Spring Boot for microservices when:
- Complex enterprise requirements
- Team has Spring expertise
- Existing Spring ecosystem investments
- Services run continuously
The best approach is hybrid: match the framework to the service’s requirements, not your comfort zone. I reduced my cloud costs by 26% and improved cold starts by 99% for the right services.
One final insight from my journey: a developer on Reddit said “I use Quarkus for microservices and Spring Boot for monoliths” - and there’s wisdom in that practical separation. It’s not about choosing one framework forever. It’s about choosing the right tool for each job.
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:
- 👨💻 Quarkus Documentation
- 👨💻 Spring Boot Documentation
- 👨💻 GraalVM Native Image
- 👨💻 Reddit Discussion: Quarkus vs Spring Boot for Microservices
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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