What is Spring AI and what problem does it solve?

Spring AI is a framework in the Spring ecosystem that provides a portable, production-ready API for integrating large language model (LLM) capabilities into Java and Kotlin applications. It was created to solve a very concrete problem: every AI provider — OpenAI, Anthropic, Mistral, Ollama, Google Vertex — ships its own SDK with different method signatures, authentication patterns, and response shapes. Without Spring AI, your Java code is tightly coupled to that specific provider, making it painful to switch or even experiment with alternatives.

Spring AI solves this by introducing a common set of interfaces — ChatModel, EmbeddingModel, ImageModel — that all provider integrations implement. Application code programs to those interfaces. When you need to swap OpenAI for Azure OpenAI, it becomes a dependency and configuration change rather than a codebase rewrite. This mirrors exactly what Spring Data did for database access and what Spring Security did for authentication.

Beyond the portability layer, Spring AI standardises the patterns that every team building AI features ends up writing from scratch: prompt templating, multi-turn conversation memory, Retrieval-Augmented Generation (RAG), structured output extraction, and function/tool calling. Having these patterns provided by the framework means teams can focus on business logic instead of plumbing.