Sunday, November 30, 2025

Traditional Threads vs. Virtual Threads: A Performance Benchmark on Spring Boot 4

Bottom line: For high-concurrency Spring Boot apps on modern Java, virtual threads usually deliver higher throughput, lower latency, and better resource efficiency than traditional threads—provided you enable them correctly and benchmark against your real workload.

Java’s concurrency story changed with Project Loom and virtual threads, and Spring Boot 4 (evolving from the Spring Boot 3.2+ line on Java 21+) is designed to take advantage of this new model. This post breaks down how traditional threads and virtual threads differ, how they behave under load in a Spring Boot service, and what to look for when running your own performance benchmarks.

What Are Traditional Threads in Java?

Traditional Java threads map one-to-one to operating system threads and are scheduled directly by the OS. Each platform thread consumes a relatively large stack and incurs non-trivial context-switching overhead, which limits how many concurrent requests your Spring Boot service can handle efficiently.

In a typical Spring Boot app using the Servlet stack (Tomcat, Jetty, or Undertow), each incoming HTTP request is processed by a worker from a fixed-size thread pool. Under high concurrency (for example, thousands of simultaneous requests), that pool can saturate, leading to increased queueing, higher tail latency, and rejected tasks.

What Are Virtual Threads (Project Loom)?

Virtual threads are lightweight threads introduced as a standard feature in Java 21 as part of Project Loom. They are scheduled by the JVM and multiplexed over a much smaller number of carrier (OS) threads, allowing your application to create hundreds of thousands of concurrent threads without overwhelming the operating system.

When a virtual thread performs blocking I/O, it parks and detaches from its carrier thread so the OS thread can do other work, which greatly improves scalability for I/O-bound workloads such as HTTP calls and database queries. Benchmarks and research show that, in these scenarios, virtual threads often deliver higher throughput and lower latency compared to traditional thread pools.

How Does Spring Boot Use Virtual Threads?

Recent Spring Boot versions (3.2+ and their Spring Boot 4 successors) support virtual threads as a first-class execution model for handling requests and background tasks. Instead of relying solely on a limited pool of platform threads, Spring can be configured to run each request on its own virtual thread, effectively enabling “one request = one virtual thread.”

Under the hood, Spring integrates virtual threads into its task executors and servlet container configuration, while letting you keep the familiar imperative programming model of Spring MVC. That means you can write synchronous code that looks blocking, but scales similarly to highly optimized reactive stacks for many I/O-heavy applications.

Performance Benchmark Setup on Spring Boot + JDK 21

To compare traditional threads vs. virtual threads in a realistic way, you need a repeatable benchmark setup. A typical environment includes:

Java 21 with virtual threads enabled in your runtime configuration.
A Spring Boot HTTP API that performs blocking I/O, such as JDBC queries or calls to external REST services.
A load-testing tool (for example, JMeter, Gatling, or k6) generating thousands of concurrent requests.

You then run the same application in two modes—one using platform-thread pools and one using virtual threads—and collect metrics such as requests per second, p95/p99 latency, memory usage, and CPU utilization under identical load.

Benchmark Results: Throughput, Latency, and Memory

Public benchmarks and academic analyses reveal consistent patterns when comparing the two threading models.

Throughput: Virtual-thread-based servers often handle significantly more requests per second, especially for I/O-bound workloads, sometimes achieving 1.5x–2x throughput compared to traditional thread pools.
Latency: High-percentile latencies (p95, p99) are typically lower with virtual threads because fewer requests are stuck waiting in queues while thread pools are exhausted.
Memory footprint: Virtual threads use far less memory per thread than platform threads, which helps keep total memory usage under control at very high concurrency levels.

Some Spring and Netty benchmarks even show virtual-thread-based setups performing competitively with or better than reactive programming models for many real-world APIs. However, if you spawn huge numbers of virtual threads without any backpressure and with a small heap, you can still hit memory limits or garbage-collection pressure, so careful tuning remains important.

Example: Configuring Virtual Threads in Spring Boot

While the exact configuration can vary by version, the general pattern in a Spring Boot 4-style application on Java 21 is to enable virtual threads in your thread executor.

Conceptually, you will:

Enable virtual-thread support via Spring properties or configuration (for example, a setting that tells Spring to use a virtual-thread-based executor).
Customize your TaskExecutor or AsyncTaskExecutor bean to create a new virtual thread per task, so each request runs in its own lightweight thread.

With this setup, your controllers can stay imperative and blocking, while the underlying runtime uses virtual threads for scalable concurrency. You can then run your benchmarks by toggling between the virtual-thread executor and a traditional thread-pool executor to see the difference in your own metrics.

When to Choose Virtual Threads vs. Traditional Threads

Virtual threads are a powerful tool, but they are not a universal solution to every performance problem.

Virtual threads are usually the better choice when:

Your Spring Boot APIs are primarily I/O-bound and must support large numbers of concurrent users.
You want to keep simple, synchronous Spring MVC code instead of adopting a full reactive programming model.
Your production environment already runs on Java 21 or later and uses Spring Boot 3.2+ / 4-era baselines.

Traditional threads may still make sense when:

Your workload is CPU-bound and the bottleneck is raw processing time rather than blocking I/O.
You depend on libraries that are not yet virtual-thread-friendly or rely on thread-local behavior that assumes a small fixed thread pool.
You cannot yet upgrade your stack to Java 21 in production.

FAQ: Virtual Threads, Loom, and Spring Boot 4

Q1. Are virtual threads always faster than traditional threads?
No; they provide the biggest gains for I/O-bound, high-concurrency workloads, but CPU-bound tasks may see limited improvement compared to well-tuned platform-thread pools.

Q2. Do virtual threads replace reactive frameworks like WebFlux?
Virtual threads remove many of the original reasons to adopt reactive-only programming, but reactive stacks still offer advantages for streaming, complex backpressure, and some specialized use cases.

Q3. What Java version is required for virtual threads in Spring Boot?
Virtual threads are a standard feature in Java 21, and modern Spring Boot lines are optimized to use them, so Java 21 or later is strongly recommended.

Q4. How can I verify that my Spring Boot app is actually using virtual threads?
You can inspect thread names, use profiling tools, or enable detailed logging around your executors and servlet container to confirm that request-handling threads are virtual.

Q5. Can virtual threads cause memory or stability issues?
They can if your application spawns massive numbers of virtual threads with no limits and runs with an undersized heap; you still need sensible backpressure, resource limits, and monitoring.

───

📥 Get Your Free Java Interview Question Guide

Don't just read—learn! Get 10 essential Java interview questions with complete solutions delivered to your inbox. Perfect for interview prep or skill refreshing.

Enter your email below:

</form>

<p style="font-size:12px; color:#666; margin-top:8px;">We'll send you the PDF plus Java tips. Unsubscribe anytime.</p>

Stop Paying for Cold Starts: Building Instant-Startup Serverless Java Functions with GraalVM

For most Java teams running on AWS Lambda, Azure Functions, or Google Cloud Functions, cold starts are the silent tax that slows user experience and inflates cloud bills. By compiling your Java functions to GraalVM Native Image and deploying them as serverless custom runtimes, you can slash cold start times from seconds to a few hundred milliseconds—and, in many cases, make them feel instant.

This post explains why Java suffers from cold starts in serverless, how GraalVM fixes the problem, what a GraalVM-powered function architecture looks like, and the key steps to build instant-start serverless Java functions with real-world tools and patterns.

Why Java Serverless Struggles With Cold Starts

On a standard JVM runtime, a serverless function has to spin up the JVM, load classes, initialize frameworks, and warm up the JIT compiler before it can handle traffic efficiently. That overhead can easily push cold starts into the hundreds of milliseconds or even seconds, especially for Spring, Hibernate, and other heavyweight stacks.

In multi-hop serverless architectures (for example, API Gateway → Lambda → database), those cold starts accumulate and visibly slow down user-facing APIs, cron jobs, and event-driven workflows. This is why many teams hesitate to choose Java for serverless—even when they love its ecosystem.

How GraalVM Native Image Kills Cold Starts

GraalVM Native Image compiles your Java bytecode ahead-of-time into a standalone, platform-specific binary that does not require a traditional JVM at startup. During build time, it performs static analysis, closes the world, and pre-initializes classes so the resulting binary can start handling requests almost immediately after the container starts.

Real-world benchmarks show native images delivering dramatically lower cold and warm start times compared to regular Java serverless functions, often cutting cold starts by multiple factors while also reducing memory usage. In low-memory configurations, Java Native Image functions often stay stable while regular JVM-based functions hit memory or performance limits.

Architecture: GraalVM-Powered Serverless Java Functions

A typical GraalVM-based serverless Java setup on a cloud function platform looks like this.

An API gateway or event source triggers a function using a custom runtime.
The function handler is compiled into a GraalVM Native Image binary packaged in a container or ZIP.
The function uses minimal, native-image-friendly libraries with reflection and resources configured for AOT.
The function connects to downstream services such as databases, object storage, or external APIs using GraalVM-compatible clients.

This architecture removes the JVM startup penalty and allows each new container to start and serve requests with near-native performance.

Example: Building a GraalVM Native Image Function

A typical workflow for building a serverless Java function with GraalVM includes several steps.

Implement a simple Java handler that conforms to your cloud provider’s function interface or HTTP handler style.
Configure your build tool with GraalVM Native Image plugins and settings.
Add reflection and resource configuration files so that frameworks and libraries work correctly under native image.
Build the native image and package it as a custom runtime or container image for deployment.

Sample repositories and reference implementations from providers and the community show this pattern end to end, including CI/CD and deployment scripts.

Performance: What the Numbers Look Like

Case studies comparing regular JVM functions to GraalVM Native Image functions highlight clear improvements in both cold and warm start performance.

Cold starts often drop from hundreds of milliseconds or seconds down to a few hundred milliseconds or less, even when using frameworks.
Warm starts become more consistent because there is no JIT warm-up and less runtime initialization overhead.
Lower memory usage allows you to choose smaller memory tiers without timeouts, reducing the cost per invocation.

In many scenarios, native Java functions become competitive with or even faster than popular dynamic-language runtimes for startup latency.

Best Practices for Instant-Startup Java Functions

To get the full benefit of GraalVM in serverless environments, you need to design with native images in mind.

Prefer frameworks that support AOT and native images, such as Quarkus or Micronaut, or use a carefully configured Spring-based setup.
Avoid unnecessary dynamic features like heavy reflection and runtime proxies unless they are explicitly supported in your native config.
Keep your function’s startup logic minimal by pushing large caches or nonessential initialization out of the cold path.

These practices help produce smaller, faster binaries that are ideal for bursty, event-driven workloads.

When GraalVM Serverless Shines (and When It Doesn’t)

GraalVM Native Image is especially compelling when latency and cost are tightly coupled to cold start behavior.

It shines when your traffic is spiky, functions are short-lived, and user-facing SLAs cannot tolerate long cold starts.
It helps when you want to run Java functions in low-memory configurations to reduce costs without sacrificing reliability.
It is less ideal when your application depends heavily on dynamic JVM features that are difficult to support in a closed-world native image.

Teams often adopt a hybrid approach, using GraalVM Native Image for the most latency-sensitive or cost-sensitive functions and the regular JVM for others.

FAQ: GraalVM, Cold Starts, and Serverless Java

Q1. Does using GraalVM lock me into one platform?
No. GraalVM can target different platforms, and native images can be deployed on multiple cloud providers that support custom runtimes or containers.

Q2. How much improvement can I expect in cold starts?
Many reports show multi-fold reductions, with cold starts shrinking to a fraction of their JVM equivalents in typical serverless setups.

Q3. Can I still run my app on a normal JVM?
Yes. Most projects can be built to run both as a regular JVM application and as a native image, depending on environment and configuration.

Q4. Does GraalVM always lower my cloud bill?
It often reduces costs for bursty workloads with many cold starts or low-memory configurations, but native image build time and complexity also need to be considered.

Q5. Is migrating an existing Java function to GraalVM worth it?
If your current serverless functions suffer from slow cold starts or require high memory to stay responsive, migrating to GraalVM Native Image is usually a high-impact optimization.

───

📥 Get Your Free Java Interview Question Guide Don't just read—learn! Get 10 essential Java interview questions with complete solutions delivered to your inbox. Perfect for interview prep or skill refreshing. Enter your email below:

We'll send you the PDF plus Java tips. Unsubscribe anytime.

Is Project Loom Finally Ready? A Deep Dive into Java Virtual Threads in Production

Bottom line: Project Loom’s virtual threads are ready for many real-world, I/O-heavy Java workloads in production—provided you benchmark carefully, validate library compatibility, and follow a few best practices for observability and resource management.

Virtual threads became a standard feature in Java 21 and are already running in production at companies building high-throughput APIs, payment systems, and Spring Boot microservices. This post explores what “production-ready” really means for Loom, how virtual threads behave under load, and how to decide if now is the right time to adopt them in your stack.

What Are Java Virtual Threads (Project Loom)?

Java virtual threads are lightweight threads introduced by Project Loom and made permanent in Java 21. Unlike traditional platform threads, virtual threads are scheduled by the JVM and multiplexed onto a smaller pool of operating-system threads, allowing you to run hundreds of thousands of concurrent tasks without huge thread pools.

The goal of Loom is to make blocking code cheap again. Instead of forcing developers into complex reactive paradigms, virtual threads let you write straightforward, imperative code that still scales under high concurrency. In practice, you keep familiar constructs like synchronized, JDBC, and HTTP clients, while the JVM optimizes scheduling under the hood.

How Do Virtual Threads Change the Concurrency Model?

Traditional Java concurrency relies on a relatively small number of heavyweight OS-backed threads. Every blocked operation ties up one of these threads, which is why frameworks introduced async and reactive patterns to avoid wasting thread resources.

Virtual threads flip this model:

Each task can run in its own virtual thread, so blocking calls simply park the virtual thread and free the carrier thread.
A small pool of carrier threads can run many more virtual threads than a pure platform-thread model would allow.
You can often replace callback-heavy or reactive code with simple, synchronous logic for I/O-bound services.

This brings Java closer to the ergonomics of goroutines in Go, but with the maturity of the JVM ecosystem.

Are Virtual Threads Really Ready for Production?

Several signals suggest virtual threads are ready for serious production use in I/O-heavy systems:

Virtual threads are a fully supported, general-availability feature in Java 21, not a preview or experimental API.
Case studies from real-world systems report successful migrations of web and backend services to Java 21 virtual threads with improved throughput and lower latency.
Frameworks such as Spring and various web stacks now include first-class support and configuration options for virtual-thread-based execution.

“Production-ready,” however, does not mean “zero-risk toggle.” You still need to benchmark your own workloads, validate every critical library, and review concurrency assumptions before making virtual threads the default everywhere.

Common Production Pitfalls (Pinning, ThreadLocals, Libraries)

Virtual threads come with new considerations you must understand before rolling them out widely.

Pinning: When a virtual thread holds a monitor lock or some blocking operations, it can become pinned to a carrier thread, reducing scalability and undermining Loom’s benefits.
ThreadLocal misuse: Heavy use of ThreadLocal can become problematic when spinning up large numbers of virtual threads, both in terms of memory and unexpected behavior. Newer mechanisms like scoped values can help here.
Library assumptions: Some libraries assume a small, fixed number of threads or use blocking patterns that do not scale well when each request becomes its own virtual thread.

Profiling for pinning, auditing library behavior, and refactoring synchronized hotspots are critical steps on the path to safe production adoption.

How to Enable Virtual Threads in Real Web Apps

In a typical Java 21 web stack, you can often enable virtual threads with limited changes to your existing code.

Common steps in a Java 21 + Spring Boot–style application include:

Switching your task executor or request executor to use a “virtual thread per task” configuration.
Enabling the appropriate framework property so controllers and services run on virtual threads instead of a fixed platform-thread pool.
Running full performance and resilience tests to compare throughput, latency, and resource usage against your previous configuration.

This approach lets you keep your current business logic and APIs while changing only the concurrency model underneath.

Best Practices for Using Virtual Threads in 2025

Teams that have deployed virtual threads in production have converged on a set of practical guidelines.

Start with I/O-bound workloads: Apply virtual threads first to services dominated by network and database calls, where the benefits are clearest.
Use structured concurrency: Adopt Java’s structured concurrency APIs to manage related tasks as cohesive units, simplifying cancellation, error handling, and observability.
Upgrade monitoring: Ensure your logging, metrics, and tracing tools can handle large numbers of virtual threads and highlight pinning, contention, and slow operations.

A gradual rollout—beginning with a lower-risk service, gathering metrics, then expanding—lets you learn safely while gaining real production data.

FAQ: Loom, Virtual Threads, and Production Readiness

Q1. Do virtual threads replace reactive frameworks completely?
Not entirely. Virtual threads remove many reasons to reach for reactive patterns in simple I/O-bound services, but reactive frameworks still shine for streaming, advanced backpressure, and certain specialized scenarios.

Q2. Are virtual threads suitable for CPU-heavy workloads?
They work, but you may not see significant gains because CPU-bound tasks are limited by cores, not by thread count. Dedicated pools or platform threads may still be a better fit for pure compute pipelines.

Q3. How can I detect pinning problems in my application?
Use profilers and JVM diagnostics that surface pinned virtual threads and look for synchronized blocks, blocking calls, or shared locks that keep carrier threads busy.

Q4. Is upgrading to Java 21 worth it just for virtual threads?
If you operate large-scale, I/O-heavy services and currently juggle complex async code or large thread pools, the upgrade often pays off in simpler code, better scalability, and improved tail latency.

Q5. So, is Project Loom finally production-ready?
Yes—for many modern Java 21 services that are primarily I/O-bound and use compatible libraries, virtual threads are mature enough for production, as long as you test thoroughly, monitor carefully, and roll out changes in stages.

The Java Developer's Guide to Prompt Engineering: Supercharge Your Code with AI

The Java Developer’s Guide to Prompt Engineering: Supercharge Your Code with AI

Prompt engineering in Java is no longer just “writing good prompts” — it is a concrete skill that lets you design stable, repeatable AI behaviors directly from your code. With the right prompt patterns, you can turn large language models into Java-savvy copilots, code generators, test writers, and documentation engines that plug straight into your existing stack.

For Java developers working with LLMs through frameworks or custom REST clients, learning prompt engineering is the fastest way to improve accuracy, reduce hallucinations, and ship AI features that feel production-ready.

What Is Prompt Engineering for Java Developers?

Prompt engineering is the process of designing, structuring, and testing the inputs you send to an LLM so it consistently produces the outputs your Java application needs. Instead of sending a vague string such as “write a REST controller,” you define roles, constraints, formats, and examples that the model must follow.

In code, prompts become part of your API contract with the model: they specify types, error handling rules, logging formats, and how the result should be returned to your Java application (for example, JSON, Markdown, or source code). Good prompts reduce downstream parsing headaches and make your AI features easier to maintain.

Why Prompt Engineering Matters in Java Projects

For Java teams, prompt engineering has very specific payoffs across the development lifecycle.

In backend services, prompts control how reliably models generate DTOs, SQL, or OpenAPI specs that your code can consume.
In developer tooling, prompts power AI-assisted code reviews, refactoring suggestions, and automated test creation.
In user-facing features, prompts shape chatbots, assistants, and RAG systems that interact with your customers through your Java APIs.

Without intentional prompt design, your Java app becomes fragile, constantly patching around inconsistent model output.

Core Prompt Patterns Java Developers Should Know

Several prompt patterns have emerged as especially useful for Java-centric use cases.

Role-based prompts: Tell the model to act as a “senior Java backend engineer” or “JUnit expert” to bias its outputs towards relevant patterns.
Structured output prompts: Ask explicitly for valid JSON or a specific class-like structure, so your Java code can parse the model output safely.
Step-by-step reasoning prompts: Encourage the model to think in ordered steps before producing final code or explanations, which often improves correctness.

Combining these patterns lets your Java application orchestrate complex AI tasks while keeping tight control over formats and expectations.

Integrating Prompt Engineering with Java Frameworks

Modern Java AI frameworks make it easier to embed prompt engineering directly into your application architecture.

You can encapsulate prompts inside service classes, configuration properties, or message templates and reuse them across controllers.
You can combine prompts with tools, retrievers, and memory to implement multi-step reasoning workflows in pure Java.
You can build typed wrappers around prompts so that each method represents a specific AI capability (for example, “generateOpenApiSpec” or “suggestRefactorings”).

Treating prompts as first-class configuration, rather than raw strings scattered through code, makes your AI layer testable and maintainable.

Best Practices: Writing High-Quality Prompts for Java

High-quality prompts share common traits regardless of the exact framework.

Be explicit about the task and audience: Specify whether the output is for humans, machines, or both, and whether it should be production-ready, experimental, or instructional.
Define input and output formats: Describe the expected fields, types, and examples, especially when generating code, JSON, or configuration for your Java services.
Provide constraints and failure modes: Specify what the model should do when missing data or encountering ambiguous requirements (for example, “ask clarifying questions” or “return a validation error structure”).

Iterating on these dimensions quickly improves stability and reduces the need for brittle post-processing.

Insight: Prompts Are Part of Your API Design

From a Java architect’s perspective, prompts are not just strings — they are part of your public and internal APIs. Whenever your backend depends on an LLM to generate code, SQL, or JSON, the prompt defines the contract just as much as a Java interface or REST schema does.

Designing prompts with the same discipline used for interface design (versioning, documentation, examples, and tests) helps your AI-powered features remain stable as your system evolves and models change.

FAQ: Prompt Engineering for Java Developers

Q1. Do Java developers really need to learn prompt engineering?
Yes. Prompt engineering directly affects how reliably your Java applications can use LLMs for code generation, reasoning, and automation, and it quickly becomes a core backend skill.

Q2. How is prompt engineering different from normal API design?
Prompt engineering is more probabilistic and language-driven, but like API design it still requires clear contracts, examples, and constraints to get predictable behavior.

Q3. Can prompts be tested in automated Java test suites?
Yes. Many teams now store prompts as configuration and use integration tests to assert the shape and basic behavior of model responses for critical flows.

Q4. Will better models make prompt engineering obsolete?
As models improve, prompts may become more forgiving, but clear, well-structured prompts will remain key for controlling cost, format, security, and reliability in production Java systems.

Beyond the Basics: Implementing Retrieval-Augmented Generation (RAG) in Java

Beyond the Basics: Implementing Retrieval-Augmented Generation (RAG) in Java for Real-World AI Applications

As GenAI systems move into mainstream enterprise workloads, Retrieval-Augmented Generation (RAG) has become a foundational pattern rather than an experimental concept. In 2025, Java developers building AI copilots, intelligent search, knowledge assistants, and chatbot platforms cannot afford to ignore RAG. It solves one of the biggest limitations of LLMs—hallucination—by grounding every response in trusted, organization-specific knowledge.

With modern JVM-based frameworks now offering native LLM and embeddings support, Java is no longer a step behind Python. It has become a powerful, production-ready platform for end-to-end RAG architecture, vector search, and scalable AI microservices.

Why Java Developers Should Prioritize RAG in 2025

For teams running large, mission-critical systems on Spring Boot, Quarkus, Micronaut, Kubernetes, or cloud-native microservices, RAG provides a reliable way to connect your existing application layer with enterprise knowledge sources—PDFs, Confluence spaces, Git repositories, API logs, relational databases, and more.

RAG transforms a generic LLM into a domain-aware reasoning engine that actually understands your business processes, policies, and terminology. If you are building AI-driven capabilities—recommendation engines, customer support automation, document intelligence, agent workflows, or enterprise search—RAG is no longer optional. It is the backbone of accuracy and trust.

RAG Architecture in Java: The End-to-End Flow

A production-grade RAG loop involves four continuous stages:

1. Document ingestion and chunking
Pull data from S3, SQL, NoSQL, file systems, or collaboration platforms. Break it into semantic chunks using techniques optimized for retrieval relevance.

2. Embeddings and vector storage
Convert chunked documents into embedding vectors using an LLM embedding model. Store them in high-performance vector databases like Redis, pgvector, Qdrant, Pinecone, MongoDB Atlas Vector Search, or AWS-based alternatives.

3. Retrieval and ranking
User queries are converted to embeddings, passed through a similarity search, reranked, and filtered to surface only the most relevant and authorized content.

4. Grounded generation
The final response is produced by an LLM using the retrieved context, ensuring the model stays factual, compliant, and aligned with your domain language.

This cycle powers most of today’s AI search engines, enterprise assistants, and knowledge automation solutions built in Java ecosystems.

Selecting the Right Java Stack for RAG

Two leading approaches dominate the Java GenAI landscape in 2025:

Spring AI + Spring Boot

Best fit for teams already invested in Spring. It delivers straightforward configuration of:

LLM providers
Embedding models
Vector stores
Streaming responses
AI connectors

It follows the conventions Java developers expect and integrates seamlessly with enterprise APIs, Spring Security, and existing data layers.

LangChain4j for Framework-Agnostic RAG

Ideal when you need low-level control, custom pipelines, or want to run on Quarkus, Micronaut, or standalone JVM apps. LangChain4j offers:

Composable building blocks
Flexible LLM adapters
Rich RAG utilities
Pluggable memory, tools, and vector stores

Both frameworks are mature, actively maintained, and built to power production-scale GenAI systems.

Example: A Typical RAG Service Method in Java

A simplified RAG workflow in a Java service might look like:

Embed the user’s question.
Search top-k nearest vectors in the vector store.
Construct a grounded prompt using retrieved chunks.
Submit the prompt to your LLM and deliver the result to your API/UX layer.

This clean separation allows you to evolve your RAG pipeline—switching providers, improving chunking, or tuning retrieval—without rewriting your business logic.

Beyond “Hello World”: Performance Matters

A real-world RAG system must optimize latency, relevance, and cost-efficiency. Key areas to focus on:

Semantic chunking to improve contextual accuracy.
Advanced vector search tuning (top-k, similarity metrics, ANN parameters like HNSW).
Caching, batching, and embedding reuse to reduce LLM token consumption.
Hybrid search combining keyword search + vector search for enterprise workloads.

These tuning layers often deliver more measurable gains than simply switching LLM providers.

Vector Databases: The Core Infrastructure of Java RAG

Choosing the right vector database is critical. Popular options for JVM-based RAG microservices include:

Redis Stack for high-speed, in-memory vector similarity search.
pgvector on PostgreSQL for organizations that want relational + vector search in a single DB.
Pinecone, Qdrant, Milvus for elastic, low-latency, cloud-native vector indexing.
MongoDB Atlas Vector Search for teams already using MongoDB for document storage.

Most Java AI frameworks offer direct integrations, making setup efficient and production-ready.

Security, Compliance, and Guardrails for Enterprise Java RAG

In enterprise environments, RAG must operate under strict rules: authentication, authorization, privacy policies, and business constraints. The retrieval layer must never leak documents the user is not permitted to access.

Key strategies include:

Row-level and doc-level access controls before performing vector lookups.
Prompt filtering and policy-based output moderation.
Integration with enterprise policy engines, IAM systems, and audit pipelines.

This combination ensures your RAG deployment is not just powerful—but responsible and compliant.

Why This Is the Right Moment to Build RAG in Java

With mature frameworks like Spring AI and LangChain4j, robust vector databases, and proven retrieval patterns, Java has evolved into a first-class ecosystem for building scalable, maintainable, enterprise-grade GenAI applications.

You no longer need Python scripts or external hacks. Everything—from embeddings to prompt orchestration to vector search—can live inside your existing Java microservices.

If you want your Java applications to stand out in 2025, it’s time to move beyond basic LLM wrappers. Build a production-ready Retrieval-Augmented Generation pipeline that reflects your domain expertise and delivers real business impact.

Spring AI vs. LangChain4j: Which is the Best Framework for Integrating LLMs into Your Java App?

Java Devs Finally Have a Choice

Large language models are no longer “nice-to-have” addons in Java apps—they’re becoming core features that power chatbots, copilots, smart search, and automation flows. The big question for Java developers in 2025 is simple: when integrating LLMs, should you bet on Spring AI or LangChain4j?spring+3

In this post, you will see what each framework is best at, where they struggle, and real code snippets to help you decide which one fits your next Java project.baeldung+1

What Is Spring AI?

Spring AI is the official Spring ecosystem framework for integrating AI and LLMs into Spring Boot applications using familiar Spring patterns like auto-configuration, dependency injection, and portable service abstractions. It gives you high-level clients such as ChatClient and EmbeddingClient so you can swap providers (OpenAI, Azure OpenAI, Hugging Face, and more) with minimal code changes.javacodegeeks+3

If you already live in the Spring Boot world, Spring AI feels “native”: configuration via application properties, starter dependencies, observability, and security hooks integrate nicely with the rest of your stack.spring+1

What Is LangChain4j?

LangChain4j is an open-source Java library that focuses on making LLM integration easy and modular for any Java application, not just Spring Boot. It provides a unified API over many LLM providers and vector stores (OpenAI, Gemini, Pinecone, Milvus, pgvector, and more) plus a rich toolbox for agents, RAG pipelines, memory, and function calling.github+2

Because LangChain4j does not require Spring, you can use it in Quarkus, Micronaut, plain Java, or even CLI tools, making it attractive for smaller services and framework-agnostic architectures.elastic+1

Design Philosophy: Convention vs Composition

Spring AI embraces “convention over configuration” and leans heavily on Spring Boot’s auto-configuration model. You declaratively configure providers and then inject high-level clients, letting Spring manage most of the wiring for you.infoq+3

LangChain4j takes a more explicit, building-block approach, where you compose chains, tools, retrievers, and memories yourself, giving you fine-grained control over the LLM pipeline. Recent real-world comparisons show Spring AI shines in typical enterprise Spring Boot apps, while LangChain4j often feels lighter and more flexible for custom pipelines.dev+3

Quick Start: Simple Chat Example

Here is a conceptual comparison of a simple “chat completion” in both frameworks, assuming you have added the right Maven dependencies and configured keys via environment variables or application properties.baeldung+1

Spring AI style (pseudo-style example):

Define a ChatClient bean and inject it into your service.
Call a high-level method like chatClient.generate(prompt) to get responses.

LangChain4j style (pseudo-style example):

Construct an LLM object with your provider configuration.
Build a chain or use a helper method to send prompts and handle responses.

Both approaches reduce boilerplate when talking to LLM APIs, but Spring AI hides more behind Spring Boot magic, while LangChain4j exposes more explicit objects and chains.layer5+1

RAG and Agents: Who Does What Better?

LangChain4j has focused heavily on advanced patterns like Retrieval-Augmented Generation (RAG), agents, and tool calling since its early releases, offering ready-made components for ingestion, retrievers, and vector stores. For complex agentic workflows, community resources and integrations (e.g., with Elastic, MongoDB, and Quarkus) make it easy to build production-grade pipelines.javapro+3

Spring AI has been rapidly adding patterns like advisors and LLM-as-a-judge, plus integrations for multi-provider setups and evaluation flows, which makes it strong for enterprise-y Spring Boot apps that need governance and structured evaluation. If your main scenario is “Spring Boot app + RAG + observability + security,” Spring AI is quickly becoming a very compelling default.geeksforgeeks+3

Performance and Resource Usage

Independent benchmarks and community articles indicate that LangChain4j is often slightly leaner and faster in basic scenarios like chat and streaming, with lower memory overhead, especially when not running inside a heavy Spring Boot context. However, when you are already running Spring Boot for the rest of your app, the incremental overhead of Spring AI is minimal and may be outweighed by the benefits of native integration.youtubelayer5+2

For serverless or microservices where cold start and memory are critical, combining LangChain4j with lightweight runtimes like Quarkus or native images can be a powerful choice. For monoliths or larger microservices already on Spring Boot, Spring AI’s tight integration with the Spring ecosystem can simplify deployment, monitoring, and scaling.developers.redhat+3

Example: Building a Java RAG Service

A typical RAG flow in LangChain4j would involve configuring an embeddings model, a vector store, a document loader, and a retriever, then wiring them into a chain that takes user queries and context documents. This level of explicit composition gives you freedom to swap MongoDB, Elastic, or Pinecone, and to tweak retrieval logic in detail.mongodb+3

In Spring AI, you would configure your embedding provider and vector store via Spring properties, then use Spring-managed beans to orchestrate retrieval and generation, potentially with advisors for response evaluation. This works especially well when you already rely on Spring Data, Spring Security, and Actuator for metrics and tracing.javacodegeeks+3

When Spring AI Is the Better Choice

Spring AI is generally the better framework when:

You are already using Spring Boot for your REST APIs, data access, and security.spring+1
You want “Spring-native” configuration, monitoring, and dependency injection for your AI services.infoq+1
Your organization values standardized frameworks and long-term support in the Spring ecosystem.geeksforgeeks+1

In these cases, Spring AI minimizes stack fragmentation and lets your team reuse existing Spring expertise to ship AI features faster.layer5+1

When LangChain4j Is the Better Choice

LangChain4j is usually the better fit when:

You need framework-agnostic LLM tooling for plain Java, Quarkus, Micronaut, or CLI tools.github+1
You want advanced agent/RAG tooling with fine-grained control over each step of the pipeline.javapro+1
You care about lightweight performance, cold starts, or running in non-Spring environments.dev+1

If you are building experimental AI services, side projects, or high-performance microservices, LangChain4j keeps your options open without forcing you into the Spring ecosystem.youtubemongodb

Final Verdict: “Best” Depends on Your Stack

There is no single winner—instead, “best” depends entirely on your existing stack and priorities as a Java developer. If your world is already built on Spring Boot, Spring AI is the natural, low-friction choice that keeps everything under one well-known framework.linkedin+3

If you want maximum flexibility, framework independence, and a rich toolbox for agents and RAG, LangChain4j is hard to beat in 2025. The smartest strategy is to pick the one that aligns with your architecture today, while keeping an eye on how both ecosystems evolve—because the Java AI landscape is moving very fast.github+2youtube

Spring AI vs. LangChain4j: Which is the Best Framework for Integrating LLMs into Your Java App?

Dear Reader,

In the fast-evolving landscape of Artificial Intelligence, integrating Large Language Models (LLMs) into applications has become a priority for many developers. Java, a stalwart in the programming world, offers robust frameworks to facilitate this integration. Among these, Spring AI and LangChain4j stand out as leading contenders. This article dives deep into both frameworks to help you decide which is best suited for your Java application.

Overview of Spring AI

Spring AI, an extension of the well-known Spring ecosystem, provides a comprehensive solution for integrating AI capabilities into Java applications. Leveraging Spring Boot's simplicity and scalability, Spring AI allows developers to seamlessly incorporate AI models, including LLMs, with minimal configuration.

Key Features

Seamless Spring Boot Integration: Utilizes the familiar Spring Boot setup, making it easy for developers already versed in the Spring ecosystem.
Extensive Model Support: Supports a wide array of AI models and libraries.
Robust Configuration Management: Offers extensive configuration options, utilizing Spring's powerful configuration management capabilities.

Practical Code Example


import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@SpringBootApplication
public class SpringAiApplication {

    public static void main(String[] args) {
        SpringApplication.run(SpringAiApplication.class, args);
    }

    @Bean
    public LlmService llmService() {
        return new LlmServiceImpl();
    }
}

@Configuration
class LlmConfiguration {

    @Bean
    public LlmModel llmModel() {
        return new LlmModel("path/to/your/model");
    }
}

Real-World Use Cases

Customer Support Automation: Utilize LLMs for automated customer service chatbots.
Content Generation: Automatically generate reports or articles based on data inputs.

Overview of LangChain4j

LangChain4j is a relatively new framework focused on providing intuitive tools for language model integration into Java applications. It emphasizes simplicity and ease of use, making it accessible even for those without extensive AI experience.

Key Features

Lightweight and Fast: Minimal overhead, designed for efficiency.
Intuitive API: Offers an easy-to-use API that abstracts much of the complexity.
Strong Community Support: Growing community with active contributions and support.

Practical Code Example


import langchain4j.LangChain;
import langchain4j.models.LlmModel;
import langchain4j.services.LlmService;

public class LangChain4jExample {

    public static void main(String[] args) {
        LangChain langChain = new LangChain();
        LlmModel model = langChain.loadModel("path/to/your/model");
        LlmService service = langChain.createService(model);

        String response = service.query("What is the weather like today?");
        System.out.println(response);
    }
}

Real-World Use Cases

Interactive Educational Tools: Build applications that use LLMs to provide interactive learning experiences.
Data Analysis Assistants: Enhance data analysis tools with natural language processing capabilities.

Key Features Comparison

Performance and Scalability

Both Spring AI and LangChain4j are designed to handle high-performance tasks, but their approach differs. Spring AI leverages the Spring ecosystem's scalability, providing robust performance for enterprise-grade applications. LangChain4j, being lightweight, excels in scenarios where quick deployment and low latency are crucial.

Ease of Integration

Spring AI offers a smooth integration path for existing Spring Boot projects, making it an ideal choice if you're already using the Spring framework. LangChain4j stands out for its simplicity and ease of use, particularly beneficial for new projects or developers new to AI.

Community and Support

Spring AI benefits from the extensive Spring community and resources, offering strong support and a wealth of documentation. LangChain4j, while newer, has a rapidly growing user base and active community, providing ample support through forums and collaborative platforms.

Use Cases and Suitability

When deciding between Spring AI and LangChain4j, consider your project's specific needs. Spring AI is well-suited for projects that require deep integration with existing Spring applications, while LangChain4j is perfect for lightweight, quick-to-deploy solutions.

Enterprise Applications: Opt for Spring AI for its robust infrastructure.
Startups and Prototypes: Choose LangChain4j for its speed and simplicity.

Conclusion: Choosing the Right Framework

Selecting between Spring AI and LangChain4j depends on your project's requirements and your familiarity with the frameworks. Spring AI is a natural fit for those already embedded in the Spring ecosystem, offering extensive support and scalability. LangChain4j provides a compelling option for those seeking simplicity and speed in deploying LLMs.

Future Outlook

Both frameworks are poised for growth as AI technology continues to advance. Spring AI will likely expand its feature set, while LangChain4j will continue to refine its user-friendly approach. Keeping an eye on community developments and updates will ensure you make the most out of these powerful tools.

Whether you choose Spring AI or LangChain4j, integrating LLMs into your Java application can transform how you build and interact with software, opening new avenues for innovation and efficiency.

For more insights and updates, visit our blog at thinkwithjava.blogspot.com.

Best Regards,
Your Java AI Enthusiast Team

Sunday, November 30, 2025

Traditional Threads vs. Virtual Threads: A Performance Benchmark on Spring Boot 4

What Are Traditional Threads in Java?

What Are Virtual Threads (Project Loom)?

How Does Spring Boot Use Virtual Threads?

Performance Benchmark Setup on Spring Boot + JDK 21

Benchmark Results: Throughput, Latency, and Memory

Example: Configuring Virtual Threads in Spring Boot

When to Choose Virtual Threads vs. Traditional Threads

FAQ: Virtual Threads, Loom, and Spring Boot 4

Stop Paying for Cold Starts: Building Instant-Startup Serverless Java Functions with GraalVM

Why Java Serverless Struggles With Cold Starts

How GraalVM Native Image Kills Cold Starts

Architecture: GraalVM-Powered Serverless Java Functions

Example: Building a GraalVM Native Image Function

Performance: What the Numbers Look Like

Best Practices for Instant-Startup Java Functions

When GraalVM Serverless Shines (and When It Doesn’t)

FAQ: GraalVM, Cold Starts, and Serverless Java

Is Project Loom Finally Ready? A Deep Dive into Java Virtual Threads in Production

What Are Java Virtual Threads (Project Loom)?

How Do Virtual Threads Change the Concurrency Model?

Are Virtual Threads Really Ready for Production?

Common Production Pitfalls (Pinning, ThreadLocals, Libraries)

How to Enable Virtual Threads in Real Web Apps

Best Practices for Using Virtual Threads in 2025

FAQ: Loom, Virtual Threads, and Production Readiness

The Java Developer’s Guide to Prompt Engineering: Supercharge Your Code with AI

What Is Prompt Engineering for Java Developers?

Why Prompt Engineering Matters in Java Projects

Core Prompt Patterns Java Developers Should Know

Integrating Prompt Engineering with Java Frameworks

Best Practices: Writing High-Quality Prompts for Java

Insight: Prompts Are Part of Your API Design

FAQ: Prompt Engineering for Java Developers

Beyond the Basics: Implementing Retrieval-Augmented Generation (RAG) in Java for Real-World AI Applications

Why Java Developers Should Prioritize RAG in 2025

RAG Architecture in Java: The End-to-End Flow

Selecting the Right Java Stack for RAG

Spring AI + Spring Boot

LangChain4j for Framework-Agnostic RAG

Example: A Typical RAG Service Method in Java

Beyond “Hello World”: Performance Matters

Vector Databases: The Core Infrastructure of Java RAG

Security, Compliance, and Guardrails for Enterprise Java RAG

Why This Is the Right Moment to Build RAG in Java

Java Devs Finally Have a Choice

What Is Spring AI?

What Is LangChain4j?

Design Philosophy: Convention vs Composition

Quick Start: Simple Chat Example

RAG and Agents: Who Does What Better?

Performance and Resource Usage

Example: Building a Java RAG Service

When Spring AI Is the Better Choice

When LangChain4j Is the Better Choice

Final Verdict: “Best” Depends on Your Stack

Overview of Spring AI

Key Features

Practical Code Example

Real-World Use Cases

Overview of LangChain4j

Key Features

Practical Code Example

Real-World Use Cases

Key Features Comparison

Performance and Scalability

Ease of Integration

Community and Support

Use Cases and Suitability

Conclusion: Choosing the Right Framework

Future Outlook

Slideshow