Spring AI is evolving rapidly, but today's AI ecosystem is still far more mature in Python, with richer frameworks, documentation, and community support. This article explains why I chose to learn the underlying AI concepts the long way instead of relying on higher-level abstractions—and how that decision made me a better AI engineer.
When I first started building AI-powered apps, my instinct was to reach for what I knew: Java and Spring Boot. Spring has always been my go-to for backend work, and Spring Boot offers one of the smoothest developer experiences in the Java world. So I assumed Spring AI would bring that same maturity to AI development.
It didn't—at least, not yet.
That doesn't make Spring AI a bad project. It's improving quickly and getting more capable with every release. But after building real-world AI systems, I realized I was spending more time working around ecosystem gaps than actually solving product problems.
So I made a change. And I learned a lot by taking the long way.
Spring AI does a great job of connecting AI capabilities to existing Spring applications. You can quickly hook up to:
For straightforward use cases like chat completion, embeddings, or basic document retrieval, it just works. If you're adding AI to an existing enterprise app, Spring AI is a solid, pragmatic choice.
The real challenge isn't Spring AI itself—it's the ecosystem around it.
Nearly every major AI framework, research project, and production library launches in Python first. Some examples:
When a new capability drops, the Python community usually has libraries, examples, and tutorials ready to go. The Java ecosystem often follows later.
One thing I noticed fast: the difference in learning resources.
When I hit a wall with Python libraries, I could almost always find:
For advanced Spring AI topics, resources were much thinner. That gap slows down experimentation and makes troubleshooting harder.
Modern AI apps aren't just about generating text. Real systems often involve:
Python libraries tend to surface these concepts earlier, with more community support and examples.
At some point, I realized I didn't just want to use AI frameworks—I wanted to understand how they worked.
So I stopped chasing the highest-level abstraction and started learning the fundamentals:
That foundational knowledge made every framework easier to pick up later.
It felt slower at first. More concepts. More docs. More trial and error.
But over time, I noticed something: I wasn't just learning a framework—I was learning ideas that applied everywhere. Whether I was using LangChain, LangGraph, LlamaIndex, or something else, the core concepts stayed the same.
That's a much more durable investment than memorizing a single API.
This isn't about picking a winner. Spring AI is improving fast, and it's a good fit for Java developers integrating AI into existing services.
But Python has a significant head start. That shows up in:
If you want to explore the latest in AI engineering, Python is the broader playground—for now.
These days, I use the strengths of both worlds:
Instead of forcing everything into one stack, I treat AI as its own service when it makes sense. That separation keeps each part focused on what it does best.
If you're a Java developer curious about AI, you don't need to leave your ecosystem behind to pick up Python—you're simply adding new tools to your kit.
Frameworks and libraries will evolve, but if you focus on the fundamentals—retrieval, embeddings, agents, tools, prompts, orchestration—you'll be prepared for whatever comes next.
Sometimes, the longer route builds the strongest foundation.
Join the discussion
Nothing here yet — be the first to weigh in.