Discover the advantages of using Java for AI development in regulated industries. Learn about architectural stability, performance, runtime guarantees, and more.

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Red Hat Developer

Java offers significant advantages over Python for enterprise AI applications in regulated industries. The JVM provides architectural stability through compile-time verification, mature security tooling with SAST and CVE management, and unified observability. Java frameworks like Spring AI and Quarkus with LangChain4j enable production-grade agentic applications while maintaining existing governance standards, reducing cognitive load on teams, and leveraging established Java expertise. Code-specialized LLMs also generate higher quality Java code due to strong typing and abundant well-structured training data.

The case for building enterprise agentic apps with Java instead of Python

Critical divergence in software supply chain governance and security

Architectural stability, performance, and runtime guarantees

Cognitive load and code specialized models

Creating AI applications is not just a matter of calling an API.
For advanced features, we prefer Python because of its extensive data ecosystem, and not just for its ability to call an API.
The speed of iteration and evolution of AI also makes Python more suitable for this type of development than Java. This does not mean that Java cannot be used, but as always, we choose “the most appropriate tool for the job”, which may be Python for 90% of applications, TS for 5% of them, and something else for the rest.
In my experience with SpringAI, the cognitive load is much higher when you are forced to use Java rather than Python for a AI/data’s-first project.

I agree with this article. The JVM is everywhere. To be at the level where you are delivering real business value, you are probably already running on the JVM, so why would you leave that ecosystem. The GenAI wave isn’t slowing down. The abstractions provided by Spring AI are amazing. And since the time that this article was released, there have already been dozens of additional improvements in Spring AI 2.0-m2.

Ehh okay, I get it, JVM is everywhere, yada yada.
But why Java and not Kotlin?

Interesting! Till now I used Spring AI to call the API. Time to do some research.