Spring AI: The Senior Dev's Honest Take on Java's AI Moment

Spring AI, an official Spring project, reached 1.0 GA in May 2025 and shipped 1.1 in November 2025 with 850+ improvements, including full Model Context Protocol integration. The framework integrates AI models into Java using auto-configuration, dependency injection, and vendor-portable abstractions, supporting 20+ AI model providers and 12+ vector stores behind a single API. It aims to close the gap for Java developers building AI-powered applications without switching stacks.

TL;DR Spring AIis an official Spring project that integrates AI models into Java using familiar patterns: auto-configuration, dependency injection, and vendor-portable abstractions.- It reached 1.0 GA in May 2025and shipped1.1 in November 2025with 850+ improvements, including fullMCP Model Context Protocol integration.- Supports 20+ AI model providers OpenAI, Anthropic, Google, Ollama, and more and12+ vector storesbehind a single consistent API.- If your team runs Spring Boot, you have no good reason to write raw HTTP calls to an LLM anymore. The history of programming languages is mostly a history of incumbents surviving longer than anyone predicted. COBOL still processes an estimated $3 trillion in daily financial transactions. Fortran still appears in climate models and aerospace simulations. Java, introduced in 1995, became the substrate of the enterprise world and never left. Then AI models arrived and, for a brief window, seemed to break this pattern. The default language for working with language models was Python. Not because Python is architecturally superior. Because the researchers and tooling builders who created LangChain, HuggingFace integrations, and notebook-first workflows were Python developers who built for themselves first. The Java engineer maintaining the systems that actually run the bank, the logistics platform, or the hospital records management system found themselves looking at Python SDKs for a technology their organization was already being asked to adopt. The operating assumption forming in 2023 was that AI integration required a completely different stack. That assumption turned out to be wrong. Spring AI is the evidence. The uncomfortable reality of the 2023-2024 AI wave was that Java was not in the initial conversation. The fast-moving AI tooling ecosystem was almost entirely Python-native. The mental model that formed, correctly for a time, was that building AI applications required a stack switch, not just a skill addition. Java developers who had spent careers building production-grade distributed systems found themselves watching an entire category of tooling emerge around a language they did not use day-to-day. According to the 2024 BellSoft Java Survey, roughly 74% of Java developers already use AI tools in their day-to-day work, but only 34% were using any AI framework to build AI-powered applications. The gap between using AI and building with AI is exactly where Spring AI operates. The question most teams were facing was not philosophical. It was architectural: do we maintain existing Spring Boot microservices, or do we introduce a Python sidecar service to handle every LLM call? The two-process architecture feels manageable in a prototype and becomes a maintenance liability in production. Spring AI closes that gap. It brings AI model integration into the Spring container itself, using the same patterns your team already knows. Spring AI is an application framework that connects enterprise Java applications to AI models using portable abstractions, Spring Boot auto-configuration, and familiar dependency injection patterns. It supports 20+ model providers and 12+ vector stores behind a single consistent API, with built-in RAG, tool calling, chat memory, and Model Context Protocol support. The project reached 1.0 GA in May 2025 and is production-ready today. The official description from the Spring team https://spring.io/projects/spring-ai is precise: Spring AI applies core Spring design principles to the AI domain, specifically portability and modular design, and promotes using plain Java objects as the building blocks of AI applications. Think Spring Data, but for AI models instead of databases. The same philosophy applies. You describe what you want from a model without pinning your code to a specific vendor. If you have been writing JpaRepository abstractions for years, the mental model transfers directly. Josh Long, Spring Developer Advocate at Broadcom, said it plainly at DevOps UK in 2025: building AI applications is mostly just calling models that have HTTP APIs. If you can build a Spring Boot service, you are already an AI developer. The skills transfer. The tooling just needed to catch up. Spring AI currently supports: Spring AI 2.0 is in active milestone development as of mid-2026, built on Spring Boot 4.0, Spring Framework 7.0, and a Jakarta EE 11 baseline, with Java 21 as the minimum runtime. The Spring container already knows your business logic. It has been holding your @Service beans since the day you wrote them. Now it is making introductions, placing your existing code in the same room as whatever language model you are deploying that quarter. The primary API is ChatClient , a fluent builder interface designed to feel like a sibling of WebClient or RestClient . Here is a working RAG-enabled chat endpoint with Spring AI 1.1: @RestController public class DocumentChatController { private final ChatClient chatClient; public DocumentChatController ChatClient.Builder builder, VectorStore vectorStore { this.chatClient = builder .defaultAdvisors new QuestionAnswerAdvisor vectorStore .build ; } @GetMapping "/ask" public String ask @RequestParam String question { return chatClient.prompt .user question .call .content ; } } Four things are happening here that required significant boilerplate a year ago. The QuestionAnswerAdvisor retrieves relevant documents from the vector store and injects them into the prompt automatically. The VectorStore abstraction means you can swap PostgreSQL/PGVector for Pinecone without touching this controller. The model itself is configured in application.yml . The entire thing is a standard @RestController . That is not a toy example. That is close to what a real document Q&A endpoint looks like in production. One of the more immediately useful things Spring AI does is treat provider selection as a deployment concern. To switch from OpenAI to Anthropic, you update application.yml : OpenAI setup spring: ai: openai: api-key: ${OPENAI API KEY} chat: options: model: gpt-4o Anthropic setup swap in, no code changes required spring: ai: anthropic: api-key: ${ANTHROPIC API KEY} chat: options: model: claude-sonnet-4-5 Your controllers, services, and business logic stay unchanged. That portability is the central design promise of the framework, and in practice it holds. Spring AI 1.1 introduced first-class Model Context Protocol MCP integration, and this is the most strategically significant part of the framework for teams building agentic systems. MCP is the emerging standard for interoperability between AI agents and the external tools they need to call. With Spring AI, you register existing business logic as AI-callable functions with minimal code: @Configuration public class BankingTools { @Bean @Description "Fetch the account balance for a given customer ID" public Function<BalanceRequest, AccountBalance accountBalance AccountRepository repo { return request - repo.findById request.customerId .map AccountBalance::from .orElseThrow ; } } The framework picks up this bean automatically and makes it available to the model via function calling. The AI can now invoke your business logic directly, with full type safety and no change to the underlying service implementation. Jonathan Schneider made a comparison in 2025 that the Spring community has been repeating since: function calling is to RAG what Inversion of Control was to Java development. IoC did not just clean up dependency management. It changed how teams thought about component design entirely. Function calling through MCP does something similar for the relationship between your business logic and AI orchestration. Spring AI 1.1 shipped with 850+ improvements across five milestone builds. If each improvement were one second of continuous work, that is over 14 minutes of fixes, enhancements, and documentation updates streaming out in a single release cycle. In concrete terms: 354 enhancements, 241 bug fixes, and 100 documentation improvements. This is a framework in rapid production maturation, not a research prototype catching up to itself. The fair criticism here is direct: Spring AI is only the right tool if you are already in the Spring ecosystem. The portability abstractions are genuinely useful, but they sit on top of Spring Boot auto-configuration. If your team runs Quarkus, Micronaut, or bare Vert.x, the first-class integration story does not apply to you. LangChain4j, the main Java alternative, has native Quarkus extensions maintained by Red Hat, supports a broader raw count of LLM providers out of the box 30+ versus Spring AI's 20+ , and does not require Spring Boot as a foundation. I want to name one real limitation I ran into during production evaluation: the Advisors API documentation https://docs.spring.io/spring-ai/reference/api/advisors.html is genuinely thin in places. I ended up reading Spring AI integration tests more than the reference guide to understand how chained advisors compose when you combine RAG retrieval with conversation memory. The team is improving this, but if you are onboarding a new engineer onto a Spring AI RAG pipeline today, budget extra time for that gap. On the question of performance: LLM network latency dwarfs any Java framework overhead by two to three orders of magnitude. A detailed Java ecosystem analysis from early 2026 confirmed this directly: the model round trip is always the bottleneck, not the abstraction layer. Spring AI adds no meaningful latency to an operation that already takes hundreds of milliseconds or more. One more honest thing to name: the API surface is still moving. Teams that pinned to Spring AI 1.0 found some adapter interfaces changed in 1.1. The upgrade path from 1.x to 2.0 will require careful attention given the Jakarta EE 11 baseline and Spring Boot 4 foundation shift. This is not a reason to avoid Spring AI. It is a reason to track minor versions more actively than you would Spring Data or Spring Security. | Dimension | Spring AI 1.1 | LangChain4j 1.x | |---|---|---| | Primary target | Spring Boot teams | Any JVM stack | | AI provider count | 20+ | 30+ | | Vector store count | 12+ | 30+ | | MCP support | First-class | Supported | | RAG tooling | Advisors API, ETL pipeline | Mature, granular pipeline control | | Quarkus / Micronaut support | Minimal | Native extensions | | Observability | Spring Boot Actuator native | Requires separate setup | | Tool calling style | Spring beans as functions | Explicit @Tool annotation | | GA release | May 2025 | May 2025 | The decision rule that emerges here is clean. Spring Boot team with existing observability and Spring Security setup? Spring AI is the path of least friction and lowest long-term maintenance cost. Non-Spring stack, or you need the broader provider catalog? LangChain4j is the reasonable alternative. Both are production-ready. The cost of switching six months into a project is real, so treat this as an architectural decision, not an implementation detail. If you are a backend engineer on a Spring Boot team, the question is not whether to use Spring AI. It is which capability to start with. Start with ChatClient and a single provider. Get one endpoint working end to end. Add a VectorStore when you have a genuine document retrieval use case. Do not attempt RAG, MCP, tool calling, and observability simultaneously on the first sprint. The abstractions are composable by design, and you can add each layer when you actually need it. If you are building internal tooling or copilot-style features over your platform's existing knowledge base the category I am actively evaluating at OHB for our Digital Application Platforms team , the Advisors API and chat memory support are the most immediately useful pieces. They let you build context-aware assistants without standing up a Python sidecar service and all the operational overhead that brings. For teams deciding whether to standardize on Spring AI for the long term: Spring AI 2.0 on Spring Boot 4 with Java 21 virtual threads is a serious platform for production agentic applications. The team shipped 2.0.0-M4 in March 2026 and RC1 in June 2026. General availability is close, and the migration story from 1.x is well-documented. The Spring AI Community GitHub organization, announced at Spring I/O Barcelona 2025, has also created a formal home for community integrations and experimental projects that the core team cannot absorb directly. It is a signal of a framework that is building a serious ecosystem around itself, not just a well-funded core team working in isolation. Spring AI is an official Spring Framework project that provides portable abstractions for integrating AI models into Java applications, following the same design philosophy as Spring Data or Spring Cache. It abstracts vendor-specific HTTP APIs behind consistent interfaces so your application code does not change when you switch providers. Calling an LLM API directly works fine for a simple one-off integration but creates vendor lock-in, lacks built-in support for RAG, tool calling, and chat memory, and produces code that does not compose with your existing Spring observability and testing infrastructure. Spring AI 1.0 reached General Availability on May 20, 2025, announced by Mark Pollack, Christian Tzolov, and Josh Long. Spring AI 1.1 followed in November 2025 with 850+ improvements including full Model Context Protocol support and a structured Advisors API. As of June 2026, version 2.0 RC1 is available, built on Spring Boot 4.0, Spring Framework 7.0, and a Jakarta EE 11 baseline with Java 21 as the minimum runtime. Yes. Ollama is a first-class provider in Spring AI, configured in application.yml exactly like any cloud provider. This means you can run a Spring AI application against a locally hosted Llama, Gemma, or Mistral model during development and switch to a cloud provider in production without changing any application code. The abstraction layer makes provider selection a configuration decision, not a refactor. The Advisors API is Spring AI's abstraction for encapsulating recurring AI patterns, like retrieving context from a vector store before sending a prompt Retrieval Augmented Generation , or maintaining conversation history across requests chat memory . Instead of wiring this logic manually in every controller, you register advisors once and they apply transparently to every ChatClient interaction. It is the AI equivalent of Spring AOP: cross-cutting concerns extracted from your business logic and applied declaratively. Spring AI 1.1 introduced first-class MCP support, allowing you to both consume MCP-compliant tool servers and expose your own Spring beans as MCP servers. Existing Spring-managed functions annotated with @Description become callable by any MCP-compatible AI agent, with the framework handling protocol compliance, tool registration, and multi-protocol version negotiation. OAuth2-secured MCP connections were included in the 1.1 development cycle, making production-grade deployment realistic from the start. If your team is already on Spring Boot, Spring AI integrates natively with your existing auto-configuration, Actuator observability, and Spring Security setup, and tool calling works through your existing Spring beans with no separate registration step. If you are running Quarkus, Micronaut, or you need a broader catalog of out-of-the-box LLM provider integrations, LangChain4j is the more practical choice. Both frameworks hit 1.0 GA in May 2025 and are production-ready today. The team's existing stack is the primary deciding factor, not the frameworks themselves. Java has been declared dead so many times that the obituaries have obituaries. In 1996, it was going to be displaced by browser applets doing the wrong thing. In 2010, by dynamic scripting languages. In 2015, by Go and Rust eating the systems programming space. In 2023, it was apparently going to be bypassed entirely by Python notebook culture as the substrate for AI development. What keeps not happening is the death of the enterprise JVM. Too many critical systems. Too much accumulated organizational knowledge. Too many engineers who think clearly in types and interfaces and who understand what it costs to rewrite something that is actually working. Spring AI is not a Java comeback story. Java never left. What Spring AI represents is the formal acknowledgment by the Spring team that the patterns which made Java productive in distributed systems, abstraction behind interfaces, inversion of control, configuration as a deployment concern rather than a code concern, are the same patterns that make AI integration maintainable at enterprise scale. The same principles that made Spring Data the default for database access in Java are now making Spring AI the default for model access. The developer role is not disappearing. It is going somewhere more interesting. The systems that AI agents will build in ten years will themselves need to be designed, tested, debugged, and operated by engineers who understand how abstractions compose under production load. The engineers who learned those skills building Spring Boot microservices are, it turns out, unusually well-prepared for what is coming. We just needed the framework to catch up. Sayed Ali Alkamel is a Google Developer Expert in Dart and Flutter, co-founder of Flutter MENA, and Manager of Digital Application Platforms at Oman Housing Bank. He has spoken at tech events across 22+ countries and shipped apps with 2.5M+ downloads. He writes about Flutter, AI, and the developer experience at dev.to/sayed ali alkamel.