Java and AI: Why This Legacy Language Still Matters

They’ve declared it dead. More than once. Obsolete. With baroque syntax. Not built for AI.
And yet, the Java ecosystem keeps going—quietly evolving, powering critical systems, and now stepping into the world of artificial intelligence with surprising confidence. A language once seen as past its prime is finding new purpose in the AI era—not in research labs, but in the real-world environments where performance, reliability, and scale still matter.

Rumors of Java’s death have been greatly exaggerated (again). For over a decade, the language has been declared obsolete, outdated, outpaced by newer, leaner, more “AI-native” alternatives. And yet, Java remains. Not just in legacy systems, but in modern cloud architectures, high-performance enterprise platforms, and, increasingly, in the evolving world of AI and machine learning.

So how did a 1990s-born language find relevance in an era of neural nets and generative models? The answer lies in its ecosystem, scalability, and quiet evolution, and in the fact that not every AI system is built in a lab. Some run in banks, factories, and telecom infrastructures. Places where Java still rules.

This article explores how Java is being used in and with AI today—and why it may be one of the smartest choices for real-world, production-grade AI-backed systems.

Java in the AI Landscape

When people think of AI development, Java isn’t the first language that comes to mind. Python dominates in research, startups, and AI-native tooling—from Jupyter notebooks to TensorFlow, from machine learning libraries to agentic frameworks. R has its niche in statistics and academia. Julia appeals to those chasing performance in scientific computing.

However, Java is still one of the most widely used programming languages in the world, especially in enterprise software. Financial institutions, telecom giants, logistics platforms—the systems that power modern infrastructure—still rely heavily on Java.

These are the environments where AI isn’t just about experimentation. It’s about integration. It’s about performance at scale, compliance, auditability, and long-term maintainability. And in those contexts, Java makes a lot of sense.

Java brings:

• Mature infrastructure: stable libraries, frameworks, build systems.
• Performance and scalability: through the JVM and proven concurrency models.
• Security and robustness: essential in finance, healthcare, and critical infrastructure.
• Cross-platform support: making it easier to deploy ML across systems.

In short: while Python may be where many AI models are born, Java is often where they go to work.

With the latest evolution of agentic architectures, the Java ecosystem can be used as one of the backbones of enterprise-level applications. Agentic AI is a new paradigm in building enterprise software, and Java—with its rich ecosystem—fits this paradigm well.

Java Machine Learning in Action

Java may not be the default language for data scientists—but in many enterprise environments, it’s the one that gets things done. Other JVM languages, such as Scala, are also deeply embedded into data science pipelines and tooling.

A typical machine learning pipeline in Java mirrors the structure of a full-stack system:

• Data ingestion from transactional databases or real-time streaming platforms like Apache Kafka.
• Data preprocessing and transformation handled within the JVM, often integrated with existing Java-based ETL processes.
• Model training using libraries like DL4J, Tribuo, or Smile—with outputs saved as portable artifacts.
• Inference and scoring embedded directly into Java microservices, often with REST endpoints.

This makes Java ideal for organizations where AI must fit into pre-existing infrastructure. Rather than building separate data pipelines or APIs in a different language, teams can keep everything within the same codebase, CI/CD process, observability, and security framework.

While Python may be a better fit for exploration and rapid prototyping, Java proves its value in long-term deployment. It offers consistency, control, and confidence, especially in high-stakes, customer-facing environments.

Agentic AI and Java’s Ecosystem

Spring AI is empowering Java developers to build the next generation of enterprise-grade agentic AI applications. By leveraging the inherent strengths of Java—robustness, scalability, and security—Spring AI provides a familiar and powerful framework for integrating sophisticated AI capabilities directly into new and existing systems. This allows the vast ecosystem of enterprises already relying on Java to innovate and adopt AI without abandoning their established technology stacks, bridging the gap between powerful language models and practical business processes.

At its core, Spring AI is designed for creating applications that can reason, act, and interact with their environment. A pivotal feature is “Tool Calling,” which allows large language models (LLMs) to connect with and utilize external systems and APIs. This is fundamental for agentic AI, transforming AI from a passive text generator into an active participant in business workflows. Spring AI offers a portable API that supports a wide range of AI models from providers like OpenAI, Google, and Microsoft, along with various vector databases. This flexibility enables enterprises to avoid vendor lock-in and select the best tools for their needs. Furthermore, the framework simplifies the implementation of patterns like Retrieval Augmented Generation (RAG), which is crucial for grounding AI models with an enterprise’s proprietary data for more accurate and contextually aware responses.

For modernizing existing applications, Spring AI offers a seamless integration path to infuse legacy systems with intelligent features. Instead of a complete rewrite, developers can use Spring AI to introduce AI-powered capabilities incrementally. For instance, an existing e-commerce platform can be enhanced with a sophisticated customer support chatbot that uses RAG to provide answers based on the company’s product documentation. A financial application could integrate an AI agent to analyze transaction data in real time to detect fraudulent patterns. Because Spring AI is built on the very popular Spring Framework, these new AI components can be easily wired into existing services, ensuring they benefit from the same enterprise-grade features like security, transaction management, and monitoring that the rest of the application uses.

In an enterprise context, building and managing AI agents at scale is critical. The Spring ecosystem, especially Spring Boot, excels at this by simplifying the creation of microservices. This allows for the development of modular, independently deployable AI agents that integrate smoothly into existing enterprise architectures. Spring AI also provides essential observability features for monitoring and troubleshooting AI operations in production. The adoption of standards like the Model Context Protocol (MCP) further enhances interoperability, creating a structured way for AI models to discover and interact with available tools. This powerful combination of features makes the Spring Framework an ideal choice for building sophisticated, multi-agent systems that can automate complex workflows and drive intelligent decision-making within the enterprise.

Java’s Role in the Future of AI

Java’s relevance in AI is about complementing research languages in environments where stability, integration, and compliance matter most. And that relevance is only growing.

As AI moves from proof-of-concept notebooks into production-grade systems, the ability to operationalize ML models—to make them observable, secure, maintainable—becomes just as important as building them.

Here’s where Java is increasingly playing a key role:

• AI agents and embedded intelligence: Java’s ecosystem supports chatbots, recommendation engines, and large language model interfaces that plug directly into enterprise platforms like CRMs, ERPs, and billing systems.
• Application modernization with AI features: companies already have their core applications written in Java. With the latest updates in JVM frameworks and the evolution of agentic AI, those applications can be fitted with new features backed by AI models that enhance enterprise productivity.
• Model serving: Java applications now routinely consume models trained elsewhere (e.g., in Python or R) using tools like ONNX Runtime Java or TensorFlow Java. This enables seamless integration of powerful models into mission-critical systems.
• Cloud-native deployment: Frameworks like Spring Boot, Quarkus, and Micronaut make it easier than ever to build lightweight, containerized Java services—perfect for embedding ML capabilities into scalable microservices.
• Serverless and edge scenarios: With GraalVM and ahead-of-time compilation, Java is shedding its “heavyweight” reputation and opening doors to more resource-constrained or latency-sensitive environments.

For AI teams embedded in industries like finance, telecom, healthcare, or government, this is more than convenience—it’s a requirement. They need traceability. Audit logs. Robust exception handling. Model versioning. And support for decade-old systems that still run core processes. Java offers all of that, steadily evolving to meet the demands of modern AI delivery.

Java may not be as trendy as some other languages, but it has quietly evolved and found its place in the practical, production-focused side of AI—especially in modern agentic AI architectures. It remains a smart, stable, and increasingly powerful option in the age of AI and machine learning.

Authors:

Marcin Ludzia
QA & Test Automation Lead

Wojciech Naumienko
Chief Solutions Architect