Everybody thinks of Python when they discuss developing AI applications, aided by Python’s AI libraries and the misconception that Java is not a good language for AI development. In reality, there is a Java-based way to interact with virtually every AI library. In addition, superior scalability, multi-threaded coding, and just-in-time compilation all make Java an excellent language for developing AI. In this blog post, Azul Deputy CTO and Java Champion Simon Ritter examines Java’s fit for developing AI applications.
Without a doubt, the hottest area in IT today is artificial intelligence (AI). The last few years have seen a massive increase in the capabilities of AI, investment in its use, and inclusion in pretty much everything around us.
When it comes to developing AI application development languages, everyone thinks of Python. Python has a plethora of libraries to choose from, including TensorFlow, PyTorch, Hugging Faces, and LangChain, plus APIs from OpenAI and Anthropic. Python is also popular in AI development for pretty much the same reasons that Java is so popular for enterprise applications. The Python syntax is easy to read, so sharing code is simple. Its approachability makes it very popular with AI researchers who are often more familiar with mathematical concepts than coding.
Meanwhile, misconceptions persist about Java’s ability to integrate with the libraries and frameworks listed above.
Let’s look at the differences between perception and reality.
Java-based ways to interact with AI libraries
These popular AI libraries and frameworks all have Java-based ways of interacting with them, as shown in the table below.
| AI Library or Framework | How to Use with Java |
|---|---|
| TensorFlow | Java API (from Google) REST endpoints support |
| PyTorch | Java API (from Meta) |
| Hugging Faces | Deep Java Library REST endpoints |
| LangChain | LangChain4J |
| OpenAI | SpringAI simple-openai REST endpoints |
| Anthropic | Java API (from Anthropic) REST endpoints |
As you can see from the table, the use of REST endpoints is widespread. I recently attended a conference and watched Josh Long of Pivotal present on Spring AI. He made an excellent point: most of what we do with AI today is sending text to a REST endpoint. We’ve been doing this for years in Java, and we’re very well-positioned to do this efficiently.
Another key aspect of the Java platform is Project Panama, which is designed to ease interactions between Java and non-Java APIs. Project Panama has delivered a simpler replacement for the Java Native Interface (JNI). The Foreign Function and Memory APIs now make integrating non-Java libraries into Java applications straightforward.
Java’s performance and scalability advantages
Although Python is a great language for developing small applications, Java really delivers when it comes to performance and scalability. The ability of the JVM to scale to internet-sized workloads is both well-proven and a primary reason for the platform’s popularity.
As an example, from the beginning, Java has supported the concept of multiple threads, which is baked right into the language. Over its 30-year history, multithreaded coding has become more powerful and simpler. The most recent introduction is Virtual threads, along with Structured Concurrency and Scoped Values. Python is inherently single-threaded because of the Global Interpreter Lock (GIL). The GIL prevents more than one thread from executing Python code at once. Python provides two modules to try and improve this: the threading module, which is still subject to the GIL performance restriction, and the multithreading module, which avoids the GIL issue by spawning separate Python processes to handle parallel execution. The multiprocessing module has severe scalability limitations and substantial challenges for developing co-operative data-sharing code.
Python’s other performance issue is that it is an interpreted language if using the most popular CPython runtime. Although Java is also an interpreted language, the JVM makes use of just-in-time (JIT) compilation. The JVM identifies frequently used methods and compiles them, as the application is running, into optimised native instructions. Again, with over twenty-five years of development, JVM JIT compilation can deliver performance gains that can be an order of magnitude better than when just using the interpreter. Azul has included a different JIT compiler in our Zing JVM that can improve on that even further (Kafka, for example, can handle 43% more transactions per second than the standard OpenJDK implementation).
Java users want to use Java for developing AI
This all amounts to the fact that Java is ideally placed to ride the new wave of AI applications being developed. We’re already seeing that, as companies need to scale their AI workloads to much larger numbers of users, they are looking to Java to do this in a simple and cost-effective way.
As evidence of this, 50% of participants in the 2025 State of Java Survey & Report say their organization uses Java to develop AI applications. Although this survey was targeted at Java developers, it clearly demonstrates that Java is being used for real-world AI application development.
Python will undoubtedly continue to be a primary language for AI development, but it won’t be long before Java overtakes Python for enterprise AI deployments.
Conclusion
Java continues to be the backbone for business-critical applications in the enterprise. Much of Java’s longevity and continued growth is its propensity to innovate, making it a critical piece of growing interest in Oracle Java alternatives, application performance, cloud cost optimization, and DevOps productivity. Continuing to be on the leading edge of serving engineers and their organizations, Java is also moving into the lead in AI.
