{"id":794,"count":22,"description":"Java AI development has moved from the edges of enterprise software into its core. As organizations deploy machine learning models in production, run real-time inference at scale, and build autonomous agents that interact with live systems, Java's combination of performance, maturity, and ecosystem depth makes it a serious platform for AI workloads. This surprises developers who think of Python as the default AI language. Python dominates research and data science \u2014 but artificial intelligence programming in Java is thriving in the places where reliability, latency, and enterprise integration actually matter. Major financial institutions, logistics platforms, and cloud providers run Java-based AI systems in production today. Learn why Java is a capable AI platform, what libraries and frameworks support it, how to implement AI in Java for common use cases, and where Java excels relative to alternatives. Whether you're a Java developer exploring AI or an AI practitioner evaluating JVM-based deployment, here's what you need to know. ","link":"https:\/\/www.azul.com\/zh-hans\/resources\/java-ai\/","name":"Java &amp; AI","slug":"java-ai","taxonomy":"resource_categories","parent":0,"meta":[],"acf":{"category_index_display_title":"","category_banner_image":{"ID":94020,"id":94020,"title":"weblogic-header-blue","filename":"weblogic-header-blue.jpg","filesize":263059,"url":"https:\/\/www.azul.com\/wp-content\/uploads\/weblogic-header-blue.jpg?wsr","link":"https:\/\/www.azul.com\/zh-hans\/weblogic-header-blue-2-2\/","alt":"","author":"97","description":"","caption":"","name":"weblogic-header-blue-2-2","status":"inherit","uploaded_to":0,"date":"2026-06-02 21:24:45","modified":"2026-06-02 21:24:45","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/www.azul.com\/wp-includes\/images\/media\/default.png","width":2481,"height":708,"sizes":{"thumbnail":"https:\/\/www.azul.com\/wp-content\/uploads\/weblogic-header-blue-150x150.jpg?wsr","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/www.azul.com\/wp-content\/uploads\/weblogic-header-blue-300x86.jpg?wsr","medium-width":300,"medium-height":86,"medium_large":"https:\/\/www.azul.com\/wp-content\/uploads\/weblogic-header-blue-768x219.jpg?wsr","medium_large-width":768,"medium_large-height":219,"large":"https:\/\/www.azul.com\/wp-content\/uploads\/weblogic-header-blue-1024x292.jpg?wsr","large-width":1024,"large-height":292,"1536x1536":"https:\/\/www.azul.com\/wp-content\/uploads\/weblogic-header-blue-1536x438.jpg?wsr","1536x1536-width":1536,"1536x1536-height":438,"2048x2048":"https:\/\/www.azul.com\/wp-content\/uploads\/weblogic-header-blue-2048x584.jpg?wsr","2048x2048-width":2048,"2048x2048-height":584}},"category_color":"blue","category_is_visible":true,"category_faq":{"":null,"section_head":{"logo":false,"overline":"","overline_options":{"style":"text","color":""},"headline":"","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"","description_options":{"size":"default","color":""},"ctas":false,"alignment":"left","cta_alignment":"standard"},"accordion":true,"faq_structured_data":true,"content_items":[{"acf_fc_layout":"text-list","list_items":[{"headline":"<p>Is Java Good for AI? Understanding the JVM Advantage&nbsp;<\/p>","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"<p>The short answer is yes \u2014 Java is good for AI, particularly for production AI workloads that demand stability, throughput, and operational maturity. Here&#8217;s why:<\/p>\n<ul>\n<li><strong>Performance at scale.<\/strong> The JVM&#8217;s JIT compilation produces native-speed execution for CPU-bound inference tasks. Java can match or exceed Python performance once a model is loaded and running.<\/li>\n<li><strong>Concurrency model.<\/strong> Java&#8217;s threading and async primitives (CompletableFuture, Project Loom virtual threads) allow high-throughput inference serving without the GIL constraints Python imposes.<\/li>\n<li><strong>Enterprise ecosystem.<\/strong> Spring Boot, Quarkus, Micronaut, Jakarta EE \u2014 Java&#8217;s frameworks integrate naturally with the rest of the enterprise stack. You don&#8217;t have to build a Python microservice alongside your Java backend to add AI; you build the AI into the Java service directly.<\/li>\n<li><strong>Operational tooling.<\/strong> Java&#8217;s monitoring, tracing, and observability ecosystem (Micrometer, OpenTelemetry, JFR) is mature. AI inference is another workload type, and the same tooling applies.<\/li>\n<li><strong>Type safety and maintainability.<\/strong> Production AI systems are maintained for years. Java&#8217;s static typing, IDE support, and refactoring tools help teams sustain complex AI codebases.<\/li>\n<\/ul>\n<p>Java is used for AI most effectively when inference is embedded in a larger application \u2014 recommendation engines in e-commerce, fraud detection in banking, document classification in legal tech, predictive maintenance in manufacturing. These are workloads where the AI is a component of a system, not the entire system.<\/p>\n<p>&nbsp;<\/p>\n","description_options":{"size":"default","color":""},"cta":{"link":"","appearance":"solid","color":"","show_arrow":false}},{"headline":"<p>Key Java AI Libraries and Frameworks&nbsp;<\/p>","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"<p><span data-contrast=\"auto\">The Java AI ecosystem is narrower than Python&#8217;s, but it covers the essential bases:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Deep Learning for Java (DL4J \/ Eclipse Deeplearning4j)<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/deeplearning4j.konduit.ai\/\">DL4J<\/a> is the most comprehensive deep learning framework for Java. It supports multilayer neural networks, CNNs, RNNs, and transformers, with GPU acceleration via CUDA. DL4J integrates with Spark for distributed training, making it suitable for large-scale ML pipelines. Its ND4J numerical computing library provides the NumPy-equivalent operations Java lacks natively.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Tribuo<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/tribuo.org\/\">Tribuo<\/a>, developed by Oracle, is a type-safe machine learning library for Java. It covers classification, regression, clustering, and anomaly detection, with built-in model evaluation and provenance tracking.\u00a0Tribuo\u00a0is designed for production: it records which data, features, and hyperparameters\u00a0produced\u00a0each model, making auditing and reproducibility straightforward.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Weka<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/www.weka.io\/\">Weka<\/a> is a classic ML toolkit from the University of Waikato. It includes a broad set of classification, regression, clustering, and feature selection algorithms, plus a GUI for interactive analysis. For Java developers learning AI or building prototypes, Weka is a low-friction entry point.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">ONNX Runtime for Java<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The <a href=\"https:\/\/onnxruntime.ai\/\">ONNX Runtime Java API<\/a> allows you to run models trained in\u00a0PyTorch, TensorFlow, or scikit-learn inside a Java application. This is the most practical path for many teams: train in Python, export to ONNX, serve\u00a0in Java. ONNX Runtime supports CPU, CUDA, and other hardware backends, with competitive inference performance.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">LangChain4j<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/docs.langchain4j.dev\/\">LangChain4j<\/a> is the Java equivalent of\u00a0LangChain\u00a0\u2014 a framework for building applications on top of large language models. It supports OpenAI, Anthropic, Mistral, local\u00a0Ollama\u00a0models, and others, with abstractions for RAG (retrieval-augmented generation), tool use, memory, and agent workflows. LangChain4j brings LLM-based application development natively into the Java ecosystem.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Apache Spark\u00a0MLlib<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\"><a href=\"https:\/\/spark.apache.org\/mllib\/\">Spark\u00a0MLlib<\/a>\u00a0provides distributed machine learning at scale, with a Java API alongside its Scala and Python interfaces. For teams running Spark pipelines,\u00a0MLlib\u00a0integrates ML model training directly into the data pipeline without a language context switch.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p aria-level=\"3\"><strong>Spring AI\u00a0<\/strong><\/p>\n<p><a href=\"https:\/\/spring.io\/projects\/spring-ai\"><span data-contrast=\"none\">Spring AI<\/span><\/a><span data-contrast=\"none\">\u00a0is an application framework for AI engineering, similar to LangChain4J.\u00a0It fits perfectly in the well-known Spring ecosystem. It abstracts the interfaces to use various LLM- and AI-services with one unified approach by providing POJOs as the building blocks of an application in the AI domain.<\/span><span data-ccp-props=\"{&quot;134233118&quot;:false,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n","description_options":{"size":"default","color":""},"cta":{"link":"","appearance":"solid","color":"","show_arrow":false}},{"headline":"<p>How to Code AI in Java: A Practical Overview&nbsp;<\/p>","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"<p><span data-contrast=\"auto\">Getting started with artificial intelligence programming in Java depends on your use case. Here are the three most common starting points:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Option 1: Use a pre-trained model via ONNX Runtime<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This is the fastest path to AI in a Java application. You get a model trained in Python (exported to .onnx), load it with ONNX Runtime, and run inference in Java. The integration is a few dozen lines of code, and the model performs identically to its Python counterpart.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Steps: add the\u00a0onnxruntime\u00a0dependency to your pom.xml or\u00a0build.gradle, load the model with\u00a0OrtEnvironment\u00a0and\u00a0OrtSession, prepare your input tensor, call\u00a0session.run(), and read the output.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Option 2: Train and serve with DL4J<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">If you want to implement AI in\u00a0Java\u00a0end-to-end \u2014 training included \u2014 DL4J is the primary\u00a0option. You define a\u00a0MultiLayerConfiguration\u00a0or\u00a0ComputationGraphConfiguration, add layers, configure the optimizer, and call\u00a0model.fit(). For most classification and regression tasks, this approach works well and keeps the entire system in Java.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">Option 3: LLM integration with LangChain4j<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">To create AI in Java that uses large language models, LangChain4j is the current best\u00a0option. You configure a\u00a0ChatLanguageModel, optionally wire up tools (Java methods annotated with @Tool), and build chains or agents. LangChain4j handles prompt formatting, API calls, and response parsing \u2014 you write business logic.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In all three cases, the Java development workflow is familiar: add a library, write code, compile, test, deploy. Java&#8217;s build tooling, IDE support, and testing frameworks apply directly to AI code.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","description_options":{"size":"default","color":""},"cta":{"link":"","appearance":"solid","color":"","show_arrow":false}},{"headline":"<p>Java AI in Production: Performance and Reliability Considerations&nbsp;<\/p>","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"<p><span data-contrast=\"auto\">Running AI in production in Java involves a few considerations that matter more than in development environments:<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\u2022\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\u2022&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"6\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Garbage collection and latency.\u00a0<\/span><\/b><span data-contrast=\"none\">Standard GC algorithms<\/span><span data-contrast=\"auto\">\u00a0introduce stop-the-world pauses. For latency-sensitive inference \u2014 where a GC\u00a0pause\u00a0mid-request causes a user-visible delay \u2014 this is a real concern. Production AI teams often tune GC aggressively or choose GC algorithms designed for low pause times.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\u2022\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\u2022&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"7\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">JVM warmup.\u00a0<\/span><\/b><span data-contrast=\"auto\">JIT compilation means Java applications run slower until the JIT has compiled hot paths. For inference servers that must perform well\u00a0immediately\u00a0after startup (e.g., after a rolling deployment), warmup time can matter.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\u2022\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\u2022&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"8\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Memory management for large models.\u00a0<\/span><\/b><span data-contrast=\"auto\">Large models loaded into JVM heap can create pressure on GC. Off-heap storage (DirectByteBuffer,\u00a0MemorySegment) is often used for model weights.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\u2022\" data-font=\"\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\u2022&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"9\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Thread management for concurrent inference.\u00a0<\/span><\/b><span data-contrast=\"auto\">High-concurrency inference is a good fit for Java&#8217;s threading model. Loom virtual threads make it practical to handle thousands of concurrent inference requests without blocking.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">These are solvable problems \u2014 and Java&#8217;s operational maturity means the tooling to solve them (profilers, GC tuning guides, heap analysis tools) is well-developed.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","description_options":{"size":"default","color":""},"cta":{"link":"","appearance":"solid","color":"","show_arrow":false}},{"headline":"<p>Java vs Python for AI: Choosing the Right Platform&nbsp;<\/p>","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"<p><span data-contrast=\"auto\">Python dominates AI research and data science because of its library ecosystem (PyTorch, TensorFlow, scikit-learn, NumPy, pandas) and the culture of the machine learning research community. For exploration, data analysis, and model training, Python&#8217;s tooling is unmatched.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Java is used for AI most effectively in different contexts: when inference is embedded in a Java backend, when the operational requirements favor JVM tooling, when the team is primarily Java-focused, or when the AI component must integrate deeply with existing Java systems.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In practice, many production AI systems use both: Python for model training and experimentation, Java (via ONNX or a model serving layer) for inference in production. This split-language approach captures the strengths of each without forcing an all-or-nothing decision.<\/span><\/p>\n","description_options":{"size":"default","color":""},"cta":{"link":"","appearance":"solid","color":"","show_arrow":false}},{"headline":"<p>Conclusion&nbsp;<\/p>","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"<p>Java AI development is a mature and growing practice. Whether you&#8217;re using DL4J for end-to-end ML, ONNX Runtime to deploy Python-trained models in Java, Tribuo for production-grade supervised learning, or LangChain4j to build LLM-powered applications, the JVM is a capable foundation for intelligent systems.<\/p>\n<p>Java&#8217;s strengths in performance, concurrency, and operational tooling make it particularly well-suited for production AI workloads \u2014 the kind where reliability and latency matter as much as model accuracy. If you&#8217;re building AI that needs to run reliably at scale, Java deserves serious consideration.<\/p>\n<p>&nbsp;<\/p>\n","description_options":{"size":"default","color":""},"cta":{"link":"","appearance":"solid","color":"","show_arrow":false}},{"headline":"<p>How Azul Can Help&nbsp;<\/p>","headline_options":{"color":"","size":"","weight":"","width":"","gradient_text":{"use_gradient_text":false,"start_color":"","end_color":""}},"description":"<p><span data-contrast=\"auto\">If\u00a0you&#8217;re\u00a0running Java-based AI workloads in production, the JVM you\u00a0run on\u00a0matters more than most teams realize. AI inference is latency-sensitive \u2014 a stop-the-world garbage collection pause during a model serving call is a user-facing failure, not just a performance hiccup.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Azul Zing&#8217;s C4 (Continuously Concurrent Compacting Collector) garbage collector\u00a0eliminates\u00a0stop-the-world pauses entirely. Unlike\u00a0<\/span><span data-contrast=\"none\">Standard GC algorithms<\/span><span data-contrast=\"auto\">, C4 runs all GC phases concurrently with application threads \u2014 meaning inference response times stay predictable under load, even during full heap collections. For teams running Java AI inference at scale, this is a meaningful operational advantage.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Azul Prime, Azul&#8217;s commercial JVM platform, combines Zing with ReadyNow technology that accelerates JIT warmup. This is directly relevant for AI services: a model serving endpoint that reaches peak performance immediately after deployment, rather than after a warmup period, behaves more reliably in production environments with rolling restarts.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","description_options":{"size":"default","color":""},"cta":{"link":"","appearance":"solid","color":"","show_arrow":false}}]}],"block_settings":{"inline_link":"","text_color":"dark"},"block_background_options":{"block_background_color":"default","block_background_image":false,"block_background_image_sizing":"maintain-ratio","block_background_overflow":{"top":"none","bottom":"none"}},"block_spacing_options":{"block_margin_top":"default","block_padding_top":"default","block_padding_bottom":"default","block_margin_bottom":"default"}}},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.7 (Yoast SEO v28.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Java &amp; AI Archives - Azul | Better Java Performance, Superior Java Support<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.azul.com\/resources\/java-ai\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Java &amp; AI Archives\" \/>\n<meta property=\"og:description\" content=\"Java AI development has moved from the edges of enterprise software into its core. As organizations deploy machine learning models in production, run real-time inference at scale, and build autonomous agents that interact with live systems, Java&#8217;s combination of performance, maturity, and ecosystem depth makes it a serious platform for AI workloads. This surprises developers who think of Python as the default AI language. Python dominates research and data science \u2014 but artificial intelligence programming in Java is thriving in the places where reliability, latency, and enterprise integration actually matter. Major financial institutions, logistics platforms, and cloud providers run Java-based AI systems in production today. Learn why Java is a capable AI platform, what libraries and frameworks support it, how to implement AI in Java for common use cases, and where Java excels relative to alternatives. 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