A production-ready, highly-available Java AI Gateway, Broker, and Scaffolding Framework built for enterprise-grade commercial systems.

When integrating Large Language Models (LLMs) into mission-critical corporate applications (e.g., E-commerce, ERP, CRM), development teams face critical architectural bottlenecks:

1. API Instability & Rate Limits: Public LLM endpoints frequently suffer from 429 Too Many Requests or 50x Internal Server Errors, breaking business continuity.
2. Exploding Token Costs: Redundant, similar prompts continuously burn capital due to a lack of production-grade semantic caching.
3. Ecosystem Friction: While AI experimentation thrives in Python, robust core business systems operate on Java. Forcing them together introduces major operational and infrastructure overhead.
4. Lack of Billing and Governance: Out-of-the-box solutions miss offline token auditing, reactive multi-tenant rate limiting, and enterprise-grade guardrails.

Enterprise Java-AI Starter bridges this gap using the native Java AI ecosystem (LangChain4j) and an end-to-end reactive architecture (Spring WebFlux). It delivers a non-invasive AI proxy gateway that gives any Java system robust, cloud-native AI capabilities in minutes.

• ⚖️ Dynamic Multi-Model Routing: Unified abstraction layer supporting OpenAI, DeepSeek, Anthropic, and top Chinese LLMs. Requests are dynamically routed using priority tiers and weighted round-robin algorithms.
• 🛡️ Resilience-as-a-Service (Failover): Backed by Resilience4j circuit breakers. If a primary channel (e.g., DeepSeek) experiences a rate limit or service blackout, the framework executes a millisecond-level automated failover to a backup channel without disrupting client Server-Sent Events (SSE).
• 💰 Distributed Semantic Cache: Converts prompts to embeddings (≥ 0.93 similarity) to hit Redis caches directly. Reduces corporate token bills by up to 90% and slashes response latency to under 50ms.
• 📊 Offline Token Auditing: Leverages jtokkit for exact, zero-cost offline token serialization. Emits decoupled reactive Spring Events immediately after stream completion for asynchronous account credit deductions.
• ⚡ High-Performance Reactive Pipeline: End-to-end asynchronous, non-blocking flow running on Spring WebFlux and Netty, optimized to hold tens of thousands of concurrent long-lived SSE connections.
• 🖥️ Next.js Developer Portal: Package includes a full Next.js 14 + Shadcn UI management console showcasing live channel health metrics, real-time QPS trackers, token consumption distributions, and advanced RAG vector pipelines.

[ Client Systems / Next.js Admin Panel ]
              │ (HTTP / Server-Sent Events)
              ▼
    [ Reactive AppKey Rate Limiter ]
              │
              ▼
    [ Distributed Semantic Cache ] ──(Hit)──> [ Redis / 50ms Ultrafast Response ]
              │ (Miss)
              ▼
    [ Resilience4j Dynamic Router ]
       ├── Primary: DeepSeek ──(Fault/429 Error)──> [ Millisecond Failover Triggered ]
       └── Fallback: Anthropic / Qwen ──────────────────────┐
              │                                             ▼
              └───────────────────────────────────> [ Smooth SSE Token Stream ]

Activate your high-availability shield by declaring your model matrix inside your property sheets:

enterprise:
  ai:
    ratelimit:
      enabled: true
      default-qps: 5
    cache:
      semantic-enabled: true
      similarity-threshold: 0.93
    channels:
      - id: "ch-deepseek"
        provider: "DEEPSEEK"
        model-name: "deepseek-chat"
        api-key: "sk-xxxxxx"
        base-url: "https://api.deepseek.com"
        priority: 1
        weight: 90
      - id: "ch-anthropic"
        provider: "ANTHROPIC"
        model-name: "claude-3-5-sonnet"
        api-key: "sk-xxxxxx"
        base-url: "https://api.anthropic.com"
        priority: 2
        weight: 10

Inject high-availability AI routines directly into your existing Spring beans:

@RestController
@RequestMapping("/api/v1/ai")
public class ChatController {

    @Autowired
    private DynamicModelRouter modelRouter;

    @PostMapping(value = "/chat/stream", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<ServerSentEvent<String>> streamChat(@RequestBody ChatQuery query,
                                                    @RequestHeader("X-App-Key") String appKey) {
        return modelRouter.chatStream(query)
            .map(token -> ServerSentEvent.builder(token)
                .event("message")
                .build())
            .onErrorResume(e -> Flux.just(ServerSentEvent.builder("Service busy. Please try again later.")
                .event("error")
                .build()));
    }
}

Get Access

• Open Core: GitHub
• Pro: Buy Pro Version on Gumroad / Stripe

The core open-source framework is licensed under the terms of the Apache 2.0 License.

The Pro Version (including advanced failover, semantic vector cache, billing modules, and the Next.js control center) is protected under a Commercial Source Code License. Unauthorized public distribution, mirror dumping, or reselling of Pro repositories is strictly prohibited.

© 2026 Enterprise Java-AI Infrastructure. Designed by High-Availability Architects.