Ravi Kumar Ramasamy’s Post

🚀 Why Java is winning the AI Production War ☕ Scale Over Prototyping: Python is great for the "lab" and the "math." But when you need an AI agent to handle 100k concurrent requests without breaking a sweat, the JVM’s performance and Virtual Threads (Loom) are undisputed. The "Glue" of the Enterprise: 31% of developers report that more than half of their Java apps now contain AI functionality. Java isn't just "talking" to AI; it's integrating machine learning directly into the existing business logic. Mature AI Ecosystem: It’s not just about libraries anymore. With Deep Java Library (DJL), Spring AI, and mature PyTorch integrations, Java devs have the same power as the data science crowd—but with enterprise-grade tooling. Cloud Cost Optimization: 41% of enterprises are using high-performance Java platforms to slash their cloud spend. In 2026, "Efficient AI" is the only AI that survives the budget audit. 💡 The Verdict: Python built the models. Java is running the business. 🏗️ As Azul’s CTO Gil Tene puts it: “People are not playing around making little demos; they're making real applications with Java for AI.” Are you still building your AI backends in Python, or have you brought the AI home to the JVM? Let’s hear your take below! 👇 #Java2026 #GenerativeAI #SoftwareEngineering #JVM #SpringBoot #AIEngineering #CloudComputing #TechTrends

"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

🤖 Is AI only for Python developers? Not really. While Python dominates conversations around Artificial Intelligence and Machine Learning, Java is equally capable of building powerful AI solutions — especially in enterprise environments. Here’s why Java deserves more attention in AI development: ✅ Performance & Scalability – Java’s JVM optimization makes it ideal for large-scale AI systems handling millions of requests. ✅ Enterprise Integration – Many organizations already run on Java ecosystems, making AI integration smoother. ✅ Strong Libraries & Frameworks – Tools like DeepLearning4J, Weka, and Tribuo enable machine learning directly within Java applications. ✅ Production Stability – Java excels when moving AI models from experimentation to real-world production systems. ✅ Microservices & Cloud Ready – Spring Boot makes deploying AI-powered APIs reliable and scalable. Python may be great for experimentation, but Java shines when AI needs to run reliably in production. AI is not about the language — it’s about solving problems with the right tools. As a Java developer, exploring AI is not a limitation — it’s an opportunity 🚀 What are your thoughts? Can Java play a bigger role in AI’s future? #ArtificialIntelligence #Java #MachineLearning #SpringBoot #AIEngineering #SoftwareDevelopment #TechThoughts

While Python often gets the spotlight, Java is quietly becoming the backbone of enterprise AI. According to Azul's latest research: • 62% of enterprises now use Java to power AI functionality • 69% of organisations in Southeast Asia report using Java for AI coding • 41% rely on high-performance Java platforms to reduce cloud compute costs The reason? Behind every AI model lies a vast infrastructure of data pipelines, streaming platforms and distributed systems, many of which run on Java. In other words, Python may build the models, but Java increasingly runs the enterprise engine that makes AI scale. Read the full story on deeptechtimes.com. #DeeptechTimes #AI #EnterpriseAI #Java #Deeptech #DigitalInfrastructure #EnterpriseTech #ArtificialIntelligence #Python #Azul https://lnkd.in/gESuZXmG

Python is way too slow for AI at scale. TM Dev Lab just published an MCP Server Performance Benchmark, and their conclusion about Python is blunt: "Not Recommended For: Any production high-load scenario (31x slower than Go/Java)." See the full benchmark: https://lnkd.in/ekMuK5hp Here's what stood out to me: 📊 Memory performance: Go #1, Java close behind 📊 CPU performance: Java #1, Go close behind 📊 Overall winner: Go, but I'd add an important caveat Go won in a vacuum, but most medium-to-large enterprises have far more Java talent, infrastructure, and libraries than Go. For most organizations, Java is the smarter tradeoff. This isn't about Python being a bad language. I recommend Python and TypeScript to new developers. But the strengths that make Python ideal for ML prototyping become liabilities when you need enterprise integration and performance at scale. I wrote about this last year in "Python is Not the Language of AI": https://lnkd.in/ebmWaQQD The usual caveats: Benchmarks are never the full story, and MCP servers are just one segment of AI deployments. But when the performance gap is measured in orders of magnitude, it should give you pause before deploying Python for AI at scale. What's your production AI stack built on?

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.

Everyone associates AI with Python. But something interesting is happening in the Java ecosystem. ⚡ AI is supposed to be Python’s territory… right? That’s what most developers still believe. Spring AI is not just an SDK wrapper. It’s an architectural integration layer for LLM-powered systems inside Spring applications. Now we can treat an LLM like any other infrastructure component inside a Spring Boot application. Instead of writing raw HTTP calls to an AI provider and manually building JSON requests, Spring AI gives you proper abstractions. To give a gist of it - 💻 Works with major providers (OpenAI, Anthropic, Azure, AWS, Google, Ollama) 🖼️ Unified API for chat, embeddings, images, and audio 🏗️ Structured outputs → map responses directly to Java POJOs 📂 Built-in RAG, memory, and tool/function calling 🗃️ Integrates with popular vector databases (PGVector, MongoDB, Pinecone, Redis, etc.) 🤖 ChatClient API to call LLMs like any other Spring service For Java developers, this isn’t about “joining the AI hype.” It’s about integrating intelligence into existing production-grade architectures. ☕ This doc is a must/fun read for curious JAVA developers: https://spring.io/ai Do you see Java becoming a serious player in AI backends, or will Python dominate long-term? #Java #SpringBoot #SpringAI #LLM #RAG #EnterpriseArchitecture #BackendEngineering #GenerativeAI #SoftwareArchitecture #AIEngineering #BackendDeveloper #ScalableSystems #DistributedSystems #APIDevelopment

The future of Java in the AI era There’s a common narrative that: “AI belongs to Python. Java is not relevant anymore.” But in enterprise systems, the reality looks very different. Java is not going away. It is evolving alongside AI. Here’s why Java will continue to play a major role in AI-driven systems. ⸻ 1️⃣ Enterprise systems are already built on Java Most large-scale systems today run on: • Spring Boot microservices • Distributed architectures • High-performance backend systems AI is not replacing these systems. It is being integrated into them. ⸻ 2️⃣ Java is ideal for production-grade AI systems AI experiments may start in Python. But production systems require: • Scalability • Stability • Strong concurrency • Long-running services Modern Java (21+) with features like Virtual Threads makes it highly efficient for AI-integrated workloads. ⸻ 3️⃣ AI is becoming a service, not a language Today, AI is consumed via: • APIs (OpenAI, Vertex AI, etc.) • Microservices • Cloud platforms This means any backend language can leverage AI, and Java is one of the strongest in enterprise environments. ⸻ 4️⃣ Spring ecosystem is adapting to AI The ecosystem is already evolving: • Spring AI • AI integrations with REST/gRPC • Cloud-native AI pipelines This makes it easier to embed AI into existing Spring Boot applications. ⸻ The real shift is not: “Python vs Java” The real shift is: Traditional Backend → AI-Enabled Backend And engineers who understand this transition will lead the next generation of systems. ⸻ #Java #AI #SoftwareEngineering #SpringBoot #CloudArchitecture #AIEngineering

Deep Learning in Java with DJL (Deep Java Library) Introduction If your production systems operate on the JVM and you’re considering AI integration, the Deep Java Library (DJL) is a compelling option. DJL is an open-source deep learning framework tailored specifically for Java developers. Rather than developing its own engine, DJL offers a unified Java API that leverages industry-standard engines such as: - PyTorch - TensorFlow - MXNet This engine-agnostic architecture facilitates seamless backend switching with minimal code modifications, preserving the integrity of your Java application structure. Key Technical Highlights 1️⃣ Unified NDArray API DJL introduces an NDArray abstraction for tensor operations, akin to NumPy, but crafted in an idiomatic Java style. 2️⃣ Automatic Differentiation (Autograd) DJL supports gradient tracking and backpropagation for model training directly within Java. 3️⃣ Model & Trainer Abstractions High-level APIs are available for defining models, managing training loops, evaluation, and inference. 4️⃣ GPU & CPU Acceleration By delegating execution to optimised native engines, DJL ensures CUDA support and hardware acceleration. 5️⃣ Pretrained Model Zoo Access to a diverse range of models for computer vision, NLP, object detection, and more simplifies enterprise inference integration. Where DJL Fits Best DJL excels in enterprise environments characterised by: - Microservices architecture utilising Spring Boot - JVM-based infrastructure - A preference to avoid Python sidecars or REST-based model serving - The necessity for compliance, monitoring, and scaling within existing Java systems DJL enables the seamless embedding of AI into backend services, such as fraud detection, recommendation engines, NLP processing, or real-time image classification, without departing from the Java ecosystem. Java has evolved beyond a backend language; with DJL, it emerges as a first-class citizen in AI-driven systems. For enterprise Java developers exploring ML integration, DJL is undoubtedly worth evaluating. #Java #DeepLearning #MachineLearning #AI #JVM #SpringBoot #EnterpriseArchitecture #MLOps