Java for AI — Overview and Ecosystem
When people think “AI programming,” they think Python. But Java is rapidly becoming a first-class citizen in AI development — especially for enterprise applications that need to integrate LLMs, RAG pipelines, and AI features into existing Java/Spring Boot systems.
In this chapter, you’ll learn where Java fits in the AI landscape and what tools are available.
The AI Landscape for Java Developers
| Layer | Python Tools | Java Tools |
|---|---|---|
| LLM Integration | LangChain, OpenAI SDK | Spring AI, LangChain4j |
| ML Inference | PyTorch, TensorFlow | DJL (Deep Java Library), ONNX Runtime |
| Vector Databases | chromadb, pinecone-client | Spring AI VectorStore (Weaviate, Pinecone, pgvector) |
| ML Training | PyTorch, scikit-learn | ❌ Not Java’s strength |
Java’s AI sweet spot: Integrating AI (LLMs, embeddings, RAG) into production enterprise applications — not training ML models from scratch.
Key Java AI Libraries
Spring AI
The official Spring project for AI integration. Supports OpenAI, Azure OpenAI, Ollama, Anthropic, and more. If you’re already using Spring Boot, this is the natural choice.
DJL (Deep Java Library)
Amazon’s open-source library for running ML models (PyTorch, TensorFlow, ONNX) in Java. Great for inference workloads.
LangChain4j
The Java equivalent of Python’s LangChain. Provides building blocks for LLM-powered applications.
What’s Next?
In the next chapter, you’ll set up Spring AI and connect to your first LLM.
