SpringBoot AI Playground: Supercharge Your Java Apps with LLMs

As GenAI continues to reshape industries, Java developers are increasingly looking for ways to integrate LLMs (Large Language Models) into their applications—securely, reliably, and with production-grade tooling. That’s where SpringBoot AI Playground comes in.

Built on top of the rock-solid Spring Boot 3.2+ ecosystem and integrated with Spring AI, this Playground provides an elegant, developer-friendly framework to build and deploy LLM-powered services.

What is SpringBoot AI Playground?

SpringBoot AI Playground is a reference architecture and implementation framework for integrating LLMs, embeddings, and vector databases into your Spring applications. Whether you’re building intelligent chatbots, smart search engines, document verification tools, or advisor assistants—this playground gives you a head start.

Supported Models and Providers

SpringBoot AI Playground supports multiple LLM providers out of the box, giving you the flexibility to switch between models depending on your use case, pricing, and latency needs.

1. OpenAI

Supports models like:

• gpt-3.5-turbo
• gpt-4 Ideal for chatbots, summarization, and general-purpose AI.

2. Anthropic Claude (via AWS Bedrock)

Models like:

• claude-v2
• claude-v3 Known for better instruction following and document reasoning. Frequently used in compliance and verification flows.

3. Amazon Titan (via Bedrock)

Great for embedding and vector search. Seamless integration with:

• Amazon RAG (Retrieval Augmented Generation) pipelines.

4. Cohere

Focused on embedding, classification, and multilingual search capabilities. Useful in enterprise search and semantic retrieval.

5. Hugging Face Models

Local or hosted models from the Hugging Face Hub—enabling:

• Custom fine-tuned models
• Private deployment
• On-premise inference

6. Azure OpenAI

For enterprises tied to Microsoft Azure ecosystems, this provider ensures compliance, regional control, and enterprise-grade scaling.

Key Features

• Plug-and-play configuration via Spring Boot starters
• Vector database support: Weaviate, Pinecone, Milvus, and Redis
• Spring-friendly abstractions: PromptTemplate, ChatClient, EmbeddingClient
• Streaming and asynchronous calls for real-time interaction
• Secure integration with AWS IAM, KMS, and VPC
• Audit logging and analytics built-in for enterprise observability

Real-World Use Cases

• Document Verification AI – Extract, verify, and reason over PDFs and images (e.g., vendor onboarding)
• Advisor Assistant – Summarize portfolios, generate recommendations, and answer FAQs
• Vector Search APIs – Combine embeddings and search to find the most relevant documents or transactions
• 💬 Enterprise Chatbots – Trained on your internal knowledge base using RAG patterns

Final Thoughts

The SpringBoot AI Playground makes building AI-native applications feasible and delightful for Java developers. It brings LLMs into the mainstream Java world—secure, scalable, and battle-tested.

If you're exploring how to use LLMs with Spring Boot, this Playground is your launchpad.