In Part 1, we ran side-by-side benchmarks of Spring Boot vs Quarkus under default and optimized configurations, both as JVM JARs and native images. We discovered that for minimalistic applications, their startup times—even without tuning—are remarkably close, and leveraging GraalVM native images only boosts performance modestly for both frameworks.
So now the pressing question is: why do Spring Boot and Quarkus behave so similarly in simple cases, yet Quarkus often shines in cold-start scenarios in production? What architectural designs and runtime strategies underlie these different results?
In this second part, we’ll understand:
- How Spring Boot orchestrates its startup — from classpath scanning to bean lifecycle and runtime enhancements.
- What Quarkus does differently, especially with its build-time processing, reflection minimization, and native-image optimizations.
- Why, despite similar raw numbers in simple benchmarks, the frameworks’ performance diverges in more complex or resource-constrained environments.
If you want to understand the mechanics behind these startup times—and why those numbers from Part 1 are just the tip of the iceberg—let’s dive in.
How Spring Boot Starts Up
Spring Boot has become the default choice for enterprise Java development, largely because of its rich ecosystem and its “just works” developer experience. But that convenience comes at a cost: startup time. To understand why, we need to walk through what actually happens when you launch a Spring Boot application.
At the heart of Spring is dependency injection and inversion of control. Instead of developers manually wiring components together, Spring scans your application for classes annotated with @Component, @Service, @Repository, @Controller, and many others. It also reads configuration classes and auto-configurations provided by Spring Boot starters.
This process is powerful—it means you can drop a dependency into your classpath and Spring will auto-configure most of what you need. But under the hood, it involves a lot of runtime work:
- Classpath scanning: Spring Boot looks at every class in your application (and dependencies) to find candidates for beans. This scanning phase alone can be expensive in larger projects.
- Reflection: Many of Spring’s features rely heavily on reflection to inspect classes, resolve annotations, and create instances dynamically. Reflection is flexible but slower than direct bytecode execution.
- Bean lifecycle management: Once candidates are found, Spring initializes beans, resolves their dependencies, applies proxies (for things like AOP, transactions, or security), and registers them in the application context. This process requires multiple steps and sometimes involves re-wiring beans if circular dependencies are detected.
- Auto-configuration checks: One of Spring Boot’s most loved features is auto-configuration, but it comes with a price. For every possible scenario (e.g., configuring a database, setting up Jackson, enabling security), Spring evaluates conditions and initializes the necessary beans. Even if not all are used, the framework still has to check them.
When you put this all together, you realize that Spring Boot’s startup time is essentially the cost of flexibility and convention over configuration. It’s designed so that developers don’t need to think about wiring details, but the trade-off is that the application must do a lot of work at runtime before it’s ready to serve a single request.
And that’s fine in many environments. If your application is a long-running service, a few extra seconds at startup may not matter. But in environments where cold starts happen frequently—like serverless platforms—this runtime overhead becomes a significant limitation.
Digging Deeper into Spring Boot’s Startup
To truly understand why Spring Boot takes longer to start, we need to look beyond the high-level “classpath scanning and reflection” explanation and examine how its application context is built and initialized.
At the core of Spring is the ApplicationContext, which manages all beans and their lifecycle. When a Spring Boot application starts, the following phases happen:
Environment Preparation
Spring Boot prepares an Environment that merges configuration properties from multiple sources: property files, environment variables, system properties, and command-line arguments. This flexibility comes at the cost of startup overhead, since all these layers must be evaluated and combined before the application even begins creating beans.
Classpath and Annotation Scanning
The ComponentScan process walks through the application’s packages, analyzing metadata for every class to detect beans. Importantly, this isn’t limited to your own code—it extends to third-party dependencies as well. In large projects with hundreds of libraries, this step grows costly.
Bean Definition Phase
Each discovered class is registered as a BeanDefinition. These definitions act as blueprints, describing scope (singleton, prototype, request, session), dependencies, and lifecycle details. They don’t yet instantiate the beans, but prepare Spring for the next phase.
Bean Creation and Dependency Resolution Once definitions are ready, Spring instantiates beans and resolves their dependencies recursively. This dependency graph can be highly complex. Spring must carefully resolve order, sometimes re-wiring beans or detecting circular dependencies.
Enhancements with Proxies and Aspects
Features like transactions (@Transactional), caching (@Cacheable), or security (@PreAuthorize) don’t operate directly on your beans. Instead, Spring generates proxies around them using JDK dynamic proxies or CGLIB. This proxy generation adds an extra layer of work at startup.
Auto-Configuration Evaluation For every starter dependency on the classpath, Spring Boot checks conditions to decide whether to apply auto-configurations. This conditional logic—while invaluable to developers—requires significant runtime evaluation.
Event Publication and Finalization
Spring Boot then emits lifecycle events (ApplicationStartingEvent, ApplicationReadyEvent, etc.) which allow both internal components and third-party libraries to perform additional work during startup.
Why This Matters
All of this together makes Spring Boot’s startup more than just a bootstrapping process—it’s a runtime orchestration. Every bean, dependency, proxy, and configuration must be discovered, validated, and wired together dynamically before the application is ready.
That’s why startup time in Spring Boot grows non-linearly with complexity. A small “Hello World” app may start quickly, but once you add JPA, security, messaging, caching, and monitoring, the overhead multiplies.
In long-running environments, this is acceptable. But in serverless or containerized workloads—where applications are constantly starting and stopping—this orchestration becomes a significant bottleneck.
How Quarkus Starts Up
Where Spring Boot does much of its work at runtime, Quarkus takes a very different path. Its philosophy is simple: shift as much work as possible from runtime to build time. By rethinking the traditional Java framework model, Quarkus changes the startup equation entirely.
Let’s unpack what this means in practice:
1. Build-Time Processing Instead of Runtime Discovery
In Spring Boot, the container discovers beans and configurations dynamically when the application starts. Quarkus, on the other hand, pushes this discovery phase upstream into the build process.
At compile time, Quarkus extensions scan your codebase and the dependencies, identifying classes that will become CDI beans, configuration sources, REST endpoints, and more. This means that by the time your JAR or native image is produced, the framework already knows:
- Which beans exist.
- How they are wired together.
- Which proxies or interceptors are required.
As a result, there’s little to no need for runtime classpath scanning or annotation parsing. Startup becomes deterministic, with far less guesswork when the JVM process begins.
2. Minimizing Reflection
Reflection is one of the biggest bottlenecks in startup performance, and frameworks like Spring historically rely on it heavily. Quarkus sidesteps this by generating bytecode at build time.
For example:
- Instead of reflecting on annotations to find methods or inject dependencies, Quarkus generates direct, type-safe code to perform those operations.
- Many common reflection-heavy tasks—like JSON serialization, REST endpoint discovery, or dependency injection wiring—are compiled into plain Java bytecode before runtime.
This is especially important for GraalVM native images, where reflection requires special configuration and can drastically increase both memory footprint and startup time if used excessively. By precomputing everything possible, Quarkus ensures the native binary contains only what’s necessary.
3. Extension Model
Quarkus is designed around extensions, each of which knows how to configure itself at build time. For example:
- The Hibernate ORM extension analyzes your entities and generates metadata during the build.
- The RESTEasy extension sets up endpoints and serializes mappings in advance.
- The Agroal extension preconfigures database connection pools before the app even starts.
These extensions are not “just libraries”; they are tightly integrated with the Quarkus build system. This allows the framework to strip out unused code paths, reducing both startup costs and final binary size.
4. Native-Image Alignment
Spring Boot can also produce native images via GraalVM, but Quarkus was designed with this target from day one. Its build-time philosophy means:
- Reflection usage is minimized, reducing the amount of GraalVM configuration needed.
- Dead code elimination is more aggressive, producing smaller binaries.
- Metadata for frameworks like Hibernate or REST is generated ahead of time, avoiding GraalVM’s “closed-world assumption” pitfalls.
This alignment is why Quarkus often achieves faster cold starts and smaller memory footprints when deployed as native images compared to frameworks that had to adapt to GraalVM later.
5. Runtime Simplicity
By doing heavy lifting earlier, Quarkus keeps runtime lean. When the process starts, it doesn’t have to:
- Scan the classpath.
- Parse annotations.
- Evaluate auto-configurations dynamically.
Instead, it essentially boots into a pre-wired application context. Beans are already known, endpoints already mapped, and proxies already generated. This explains why Quarkus shines in resource-constrained environments like Kubernetes pods, containers that scale to zero, or serverless functions—scenarios where fast startup and low memory usage directly translate to cost savings and responsiveness.
6. The Developer Experience Trade-Off
This approach does introduce a philosophical trade-off. In Spring, dropping a new library into your classpath may immediately “just work” thanks to runtime auto-configuration. In Quarkus, because so much is decided at build time, extensions must explicitly declare how they integrate.
This means the ecosystem is still growing compared to Spring Boot’s vast library of starters. However, the trade is intentional: by standardizing how extensions contribute metadata and wiring, Quarkus ensures startup remains predictable, efficient, and native-image-friendly.
In short: Quarkus reimagines the lifecycle of a Java application. Instead of treating startup as the moment when everything is discovered, wired, and configured, it treats startup as the final step of a process that already happened during the build. The result is a runtime that feels almost precompiled—because, in many ways, it is.
Spring Boot vs Quarkus: Where They Truly Diverge
At first glance, when you run a simple “Hello World” benchmark, Spring Boot and Quarkus might seem to perform almost identically. Startup times differ by fractions of a second, memory footprints are within the same order of magnitude, and if you throw GraalVM into the mix, both benefit in similar ways.
But those numbers, as we’ve seen, are just the surface. To understand why Quarkus consistently shines in cloud-native and cold-start environments while Spring Boot holds its ground in traditional long-running deployments, we need to look at the philosophy baked into each framework.
Runtime Flexibility vs Build-Time Optimization
Spring Boot embodies runtime flexibility. It assumes your application might change, that you might want to add a library or flip a configuration without touching your build. Its runtime auto-configuration system evaluates conditions dynamically, wiring beans and applying proxies based on what’s available at that exact moment. This makes the developer experience smooth and forgiving—but it also makes startup heavy, since the application must repeatedly rediscover itself.
Quarkus, in contrast, bets on build-time optimization. It assumes that by the time you build your application, you already know what it should look like. The build process crystallizes your wiring, configuration, and extension logic into generated code. The runtime then becomes lightweight, almost static. This philosophy removes flexibility at startup in exchange for predictability and speed.
The result is a classic trade-off between convenience and performance:
- Spring Boot maximizes flexibility, paying for it at runtime.
- Quarkus maximizes efficiency, paying for it at build time.
The Cold Start Factor
This trade-off matters most in how the frameworks handle cold starts.
In environments where services run for weeks or months—traditional VMs, bare metal servers, or even some containerized workloads—startup is a minor event. The cost is amortized across days of uptime. In these cases, Spring Boot’s extra seconds at launch are barely noticeable compared to the value of its mature ecosystem and ease of integration.
In serverless environments, scale-to-zero containers, or Kubernetes pods that autoscale aggressively, startup becomes a critical metric. Every second a pod spends initializing instead of serving requests translates to latency, cost, and potentially dropped traffic. Here, Quarkus’s lean runtime shines: because so much work was done at build time, the process wakes up almost instantly, whether as a JVM app or—more dramatically—as a native image.
Complexity Scales the Difference
Another key point: the gap widens as applications grow.
A minimalistic REST endpoint won’t stress Spring Boot’s classpath scanning or auto-configuration much. But as you pile on more beans, more starters, more cross-cutting features (security, transactions, messaging, ORM), the runtime overhead compounds. Quarkus, by pushing most of that work to build time, keeps startup costs closer to constant even as complexity increases.
This explains why side-by-side benchmarks of trivial apps don’t tell the full story. The frameworks may look equal in a “Hello World,” but diverge sharply in production-scale workloads.
The Bigger Picture
Ultimately, this is not about which framework is objectively faster. It’s about choosing the right tool for the environment you’re running in.
If you are building enterprise applications that live long lives in production, where startup time is irrelevant compared to developer productivity, Spring Boot remains the gold standard. Its ecosystem is unmatched, its tooling is mature, and its “just works” model saves countless hours of configuration.
If you are targeting serverless, microservices with scale-to-zero, or cost-sensitive cloud deployments, Quarkus offers clear advantages. Its build-time-first philosophy, alignment with GraalVM, and predictable startup behavior make it a natural fit for modern, elastic architectures.
Conclusion
The reason Spring Boot and Quarkus look similar in simple benchmarks is because those tests flatten the context. They don’t capture the philosophies that each framework embodies. Spring Boot optimizes for developer convenience at the cost of heavier startup. Quarkus optimizes for runtime efficiency by frontloading work at build time.
Neither approach is “better” in the abstract. Instead, each reflects a different answer to the same question: when should the framework pay the cost of being smart—at runtime, or at build time?
And in the end, that’s what explains the divergence: Spring Boot discovers itself anew each time it starts; Quarkus arrives pre-baked, ready to run.