Spring AI 2.0.0-M1 shipped December 11, marking the first milestone built on Spring Boot 4.0, Spring Framework 7.0, and a Jakarta EE 11 baseline. The release includes 67 improvements and signals Spring’s bet on enterprise AI production—not experimentation. One U.S. manufacturing company already runs 500+ Spring developers using Spring AI in production, and this milestone positions the framework for Virtual Threads-powered AI workloads that Python can’t touch.
Platform Upgrade: Jakarta EE 11 Changes Everything
The 2.0.0-M1 release rebuilds Spring AI on top of Spring Boot 4.0 and Spring Framework 7.0, both released in late 2025. The critical shift is Jakarta EE 11 as the new baseline, contributed by Dmitry Bedrin with Paul Bakker from Netflix. This isn’t a minor version bump—Jakarta EE 11 brings 16 updated specifications built for Java 21, including Virtual Threads support in Jakarta Concurrency 3.1 and Records support across the stack.
Virtual Threads are the quiet revolution here. When you’re orchestrating dozens of concurrent LLM calls in a Spring AI application, traditional thread pools choke. Virtual Threads let you spawn millions of lightweight concurrent tasks without the overhead. For enterprise AI workloads processing thousands of requests per second, this is the difference between scaling and collapsing. Python’s async/await can’t compete with this level of concurrency.
Production Features: Redis, Vector Stores, and Cost Control
Spring AI 2.0.0-M1 ships features that separate production systems from demos. The Redis-based chat memory implementation now offers persistent conversation storage with text search, range queries, and HNSW index tuning for similarity search optimization. Azure Cosmos DB gets its own Spring Boot starter for chat memory at enterprise scale. GemFire Vector Store adds username/password authentication—finally addressing the “how do we secure this?” question every enterprise architect asks.
Prompt caching improvements align with Anthropic’s incremental caching patterns, directly cutting API costs. If you’re burning $10,000 monthly on Claude API calls, proper caching can slash that by 40-60%. These aren’t flashy features, but they’re what separates a proof of concept from a production system processing millions of dollars in LLM inference.
Spring Tools 5.0: Building AI with AI
Released December 10—one day before Spring AI 2.0.0-M1—Spring Tools 5.0 integrates with Cursor and GitHub Copilot. The framework developers are optimizing for AI-assisted development, with support for Spring Framework 7, Spring Boot 4, and an experimental embedded MCP server. There’s a certain irony in using AI coding tools to build AI frameworks, but it works. With 50% of developers using AI coding tools daily (65% in top-quartile organizations), Spring is meeting developers where they code.
The Model Context Protocol support is the sleeper feature. MCP, backed by Anthropic and gaining traction across AI providers, enables AI agent interoperability. Spring AI’s enhanced MCP client positions Java developers for the agentic AI wave without switching to Python. When multi-agent systems go mainstream, Spring developers won’t be playing catch-up.
Multi-Provider Integration: No Lock-In
Spring AI 2.0.0-M1 deepens integrations across major AI providers. Native support for the official OpenAI Java SDK simplifies GPT-4 integration. Google AI gets ThinkingLevel support for Gemini models and an updated GenAI SDK (1.30.0). Azure Cosmos DB chat memory addresses Microsoft-centric enterprises. The value proposition: start with one provider, switch providers without rewriting application code, leverage provider-specific features when needed.
Vendor lock-in is real in AI. When you build directly on OpenAI’s Python SDK or Azure’s cognitive services, migration costs explode. Spring AI’s abstraction layer means you can route requests to the cheapest or fastest provider at runtime. For enterprises managing AI budgets in the millions, this flexibility is mandatory.
What This Means for Enterprise Java
Python dominates AI research and experimentation. Java dominates enterprise production systems. Spring AI 2.0.0-M1’s bet is that enterprises deploying AI at scale need the latter more than the former. When a major U.S. manufacturing company runs 500+ developers on Spring AI in production—not in Jupyter notebooks, but in systems processing real transactions—that bet looks smart.
Jakarta EE 11’s Virtual Threads unlock concurrency that most Python frameworks can only dream about. Redis chat memory and vector store authentication solve problems enterprises actually face. Multi-provider abstraction prevents million-dollar vendor lock-in disasters. These features aren’t sexy, but they’re the infrastructure that lets AI move from PowerPoint slides to production systems handling serious load.
Migration Reality Check
Spring AI 2.0.0-M1 is a milestone release, not production-ready yet. Expect breaking changes before the GA release, likely Q1-Q2 2026. The platform upgrade from Spring Boot 3 to 4 and Spring Framework 6 to 7 requires migration work. Jakarta EE 9 to 11 involves namespace changes. But the features—Virtual Threads, production-grade chat memory, enhanced MCP support—justify the migration cost for teams serious about enterprise AI.
For Java developers building AI features, the path forward is clear. Spring AI isn’t chasing Python’s experimental edge. It’s building the infrastructure that enterprise AI will run on for the next decade. That’s a different game, and one Java plays better.
