Java Dominates AI Production with Enterprise-Grade Performance

Most developers think AI = Python. But I built AI features using Java + Spring AI. Instead of handling raw LLM text responses, I mapped AI outputs directly into Java DTOs — just like a normal backend API response. ⚡ Result: Clean, type-safe AI integration for enterprise backend systems. 🧠 Architecture User ↓ Spring Boot API ↓ Spring AI ↓ LLM ↓ DTO Response ⚙️ Tech Stack Java • Spring Boot • Spring AI • OpenAI • REST APIs 🔗 GitHub https://lnkd.in/g5BHRGWY Exploring AI + Backend Architecture in Java. #Java #SpringBoot #SpringAI #AI #BackendDevelopment #GenAI

"These frameworks allow Java developers to build, train, and deploy machine learning models without leaving the ecosystem they already know — a critical advantage in large organizations where retraining thousands of developers on an entirely new language stack would be prohibitively expensive." - Java’s Quiet AI Revolution: How a 30-Year-Old Language Is Powering the Next Wave of Enterprise Machine Learning https://lnkd.in/gyiRD-iJ

Java 25 + Spring AI 2.0 -- Java Just Entered the AI Era Honestly, I always thought AI development was a Python thing. But after exploring what Java 25 and Spring AI 2.0 can do together — I'm reconsidering that. Some things that caught my attention: ✅ Stream Gatherers are finally finalized ✅ Primitive Type Pattern Matching ✅ Spring AI now has official OpenAI SDK support ✅ MCP (Model Context Protocol) integration ✅ Full JDK 25 compatibility As a Java backend developer, this feels like the right direction. We don't have to switch ecosystems to build AI-powered applications anymore. Still early days — Spring AI 2.0 is milestone release, not production ready yet. But if you have a weekend and curiosity, it's worth experimenting with. I'm going to start exploring it with some small projects. Will share what I learn. If you're a Java developer curious about AI — now is a good time to start paying attention. What are you all using to build AI features on the backend? Would love to know 👇 #Java #SpringBoot #SpringAI #BackendDevelopment #JavaDeveloper #ArtificialIntelligence #GenerativeAI #SoftwareEngineering #Microservices #DevCommunity #Tech

“Java is dead in the age of AI.” That take didn’t age well. In 2026, Java is not fighting AI. It’s powering AI in production. Python is great for experiments. But when it’s time to scale, secure, and integrate with enterprise systems… Java takes over. If you’re a Java developer wondering where you fit in this shift, here’s the simple version: 🛠 Frameworks that matter • Spring AI – plug LLMs into Spring Boot apps cleanly. • LangChain4j – build RAG pipelines and AI workflows on the JVM. • DJL – run high-performance model inference directly in Java. 📚 What you actually need to learn • Vector databases and embeddings. • RAG for private enterprise data. • AI agents that trigger real business logic, not just chat replies. 🚀 The mindset shift Don’t just build AI features. Use AI daily. Copilot. Cursor. Smart test generation. Faster boilerplate. Better reviews. Cleaner code. AI isn’t replacing Java developers. It’s replacing developers who ignore AI. With Virtual Threads, better concurrency, and mature cloud tooling, the JVM is a solid home for production-grade AI systems. #Java #GenerativeAI #SpringBoot #SoftwareEngineering #LLM Curious to hear thoughts from Zach Wilson and Raul Junco how are you seeing Java evolve in AI production systems?

Everyone told Java developers that we would have to migrate to Python if we wanted to remain relevant in the AI world. They were wrong. For years, the story went like this: "Java is for legacy enterprise software; Python is for the future of AI." But in 2026, the tides have officially shifted. With the advancement of Spring AI, we're no longer "integrating" LLMs. We're building complex, type-safe, and scalable AI agents in the ecosystem we already trust. I’m committing fully to Spring AI. Here’s why: 1. Type Safety > "Trust Me" Code: No more guessing if an LLM response will break your frontend. Mapping AI outputs directly to POJOs is a developer's dream. 2. The RAG Factor: Retrieval-Augmented Generation is the foundation of modern AI, and Spring’s VectorStore makes it feel like just another data source. 3. Enterprise-Grade: We don’t need "prototypes." We need security, observability, and dependency injection. Spring Boot gives us the framework; Spring AI provides the brain. I’ll be sharing my journey, code snippets, and architectural patterns for building Production-Grade AI using Java. Are you sticking with the Python stack for AI, or are you bringing AI into your Java microservices this year? Let’s discuss in the comments! #SpringAI #Java #GenerativeAI #SoftwareEngineering #SpringBoot #AI2026

This is exactly why I chose Java and Spring Boot over the "trendy" alternatives. Everyone said migrate to Python for AI relevance. Turns out — we didn't have to. 🙂 Spring AI is bringing production-grade AI directly into the ecosystem Java developers already know and trust. Type safety. Dependency injection. Enterprise security. Observability. Everything Spring Boot already gives us — now with AI capabilities on top. For someone like me who is building backend systems with Spring Boot and learning toward system design and AWS — this is not a disruption. This is a natural next layer on the same foundation. Java isn't trending. It's just quietly running everything. 😄 Reposting this from Darshan R — worth a read Thank you for this enlighiting information. #Java #SpringBoot #SpringAI #BackendDevelopment #SystemDesign #GenerativeAI

Most developers think AI integration is about choosing between Java and Python. But after building production AI systems for the past year, I've learned the real challenge isn't the language – it's the mindset shift. Here's what combining traditional Java development with modern AI/ML taught me: 🔍 **Speed isn't everything** While everyone obsesses over faster models and demos, I've found that building trust and reliability matters more in production systems. ⚖️ **Architecture decisions compound** Java's enterprise-grade foundations (performance, security, integration) become critical when your AI assistant needs to handle real traffic, not just proof-of-concepts. 🔧 **Hybrid approaches win** Some of my most successful projects use Java for the backbone (Spring Boot, microservices) while leveraging Python for model training. Best of both worlds. 💡 **Production reality check** The frameworks and patterns that work in demos often crumble under enterprise constraints. Building for scale from day one saves months of refactoring. The future isn't Java vs Python for AI – it's Java AND Python, each playing to their strengths. What's been your biggest surprise when moving AI projects from prototype to production? 🚀 #JavaDevelopment #AIEngineering #MachineLearning #EnterpriseAI #ProductionSystems

Ed Donner i remember your course did hv an example whr u ported python to c++ and it improved performance mugiple order. it thn escaped me thn why are we doing ML in python. The articld below seemed relevant. But c++ dpesnt figure. Any reason?

I should keep my mouth shut but I can't This is the type of content that can lead to misinformation that causes leaders to take wrong decisions without proper care. First lets dissect the problems of the benchmark (https://lnkd.in/dyJEr7fp): 1. From the code, linked it was executed with concurrent users calling tools sequentially, which means that the fibonacci sequence computation was impacting the other users tool calls, making it impossible to correctly determine the performance of each tool individually since they were contaminated (benchmark.js/mcpSession method initializes and calls all tools sequentially) 2. Docker compose was sending healthcheck requests which compete with the benchmark requests 3. The warm up is just 10 requests to the /mcp endpoint which does not allow the interperters to perform any JIT in the actual tool endpoints, also, just 10 requests with no time constraint prevents garbage collector from running as well so the warmup is not really a warmup (run_benchmark.sh/warmup) 4. Each simulated tool call is initializing a new session, which is not how a production implementation would look like, you want to initialize session once and reuse it to call the tools individually, we know that since 1997 when HTTP 1.1 was intially released 5. Python, Java and Go code are using a recursive fibonnaci algorithm, NodeJS is running a iterative algorithm. For non-technical people, recursion is a lot slower then iteration, which means that NodeJS has an advantage here, but it get's worse, since Python is not compiled, it cannot perform the optimzations that Java and Go can to eliminate some of the costs of recursion meaning that Python is severely penalized by this implementation. 6. 3.9 million requests is the sum of the three rounds, not individual ones, which is also misleading But let's say the benchmark was actually done right, what do the results actually tells us? The answer is -> nothing <- Nothing that we didn't already know: Java runs faster than Python, so what? Now let me ask you, after you saved 20ms, what are you going to do with the 2 SECONDS spent in the actuall LLM call? That's a 100x difference between your actual problem and the time you saved. AI is a tool, so is Python and Go and Java and all of them have a role in how we work. But you need to know which tool to use and how, otherwise you will be making the same mistakes I highlighted here. Or at least hire someone that will guide you in this journey. If I got something wrong I'm more than happy to correct myself, please use the comments and let's discuss.