Building a Claude LLM Router with Java 25 Virtual Threads and Structured Concurrency

Recently, a blog post from Netflix regarding their migration to Virtual Threads caught my attention. It detailed how they achieved massive scalability improvements in their backend systems by moving away from the traditional platform thread model. This inspired me to explore how these features—specifically Virtual Threads (Project Loom) and StructuredTaskScope—work under the hood. In this tutorial, we will build a practical Claude LLM router that uses a 'racing' pattern to fetch the fastest response from multiple models, leveraging the high-speed API infrastructure of n1n.ai.

The Problem with Traditional Threads

In the traditional Java execution model, every java.lang.Thread is a wrapper around an Operating System (OS) thread (Platform Thread). This creates a 1:1 relationship that is highly resource-intensive.

Each platform thread consumes approximately 1MB of stack memory. If your application handles 200 concurrent requests, you are already using 200MB of RAM just for thread stacks. Furthermore, platform threads are expensive to create and context-switch. In I/O-intensive applications—like those calling LLM APIs—threads spend the majority of their time 'blocked,' waiting for network responses. While a thread is blocked, it still occupies that 1MB of memory and prevents the OS from using that thread for other tasks.

// Traditional thread-per-request model
ExecutorService executor = Executors.newFixedThreadPool(200);

for (int i = 0; i < 1000; i++) {
    executor.submit(() -> {
        // This thread is BLOCKED during the entire HTTP call
        HttpResponse response = httpClient.send(request);
        return process(response);
    });
}
// Request 201-1000 must WAIT - all 200 threads blocked on I/O!

Enter Virtual Threads (Project Loom)

Introduced in JDK 21 and refined in subsequent versions, Virtual Threads decouple Java threads from OS threads. They are managed by the JVM rather than the OS.

When a virtual thread performs a blocking I/O operation (like calling a Claude API endpoint), the JVM 'unmounts' the virtual thread from its carrier (platform) thread. The carrier thread is then free to execute other virtual threads. Once the I/O operation completes, the JVM 'remounts' the virtual thread and resumes execution. This M:N scheduling allows a single machine to handle millions of concurrent virtual threads, as each virtual thread only costs about 1KB of heap memory.

To upgrade your application to use virtual threads, the code change is often as simple as:

// Before: Platform threads (limited by OS)
ExecutorService executor = Executors.newFixedThreadPool(200);

// After: Virtual threads (virtually unlimited)
ExecutorService executor = Executors.newVirtualThreadPerTaskExecutor();

Structured Concurrency with StructuredTaskScope

While virtual threads solve the resource problem, managing the lifecycle of multiple concurrent tasks has historically been messy using CompletableFuture. Java 25 (Preview) introduces StructuredTaskScope to bring order to the chaos.

Structured Concurrency ensures that if a parent task fails or is cancelled, all its sub-tasks are automatically cleaned up. This prevents 'thread leaks'—a common issue where background tasks continue running long after the main request has timed out.

Practical Project: The Claude LLM Router

In the world of LLMs, developers often face a trade-off between latency, cost, and quality. Claude offers three main tiers:

1. Claude 3 Haiku: Fastest and cheapest.
2. Claude 3.5 Sonnet: The balanced powerhouse.
3. Claude 3 Opus: Most capable but slowest.

Using n1n.ai, which provides a unified interface for these models, we can build a router that 'races' these models. The goal is to send the same prompt to all three models simultaneously and return the response from whichever one finishes first, while immediately cancelling the others to save resources.

The Implementation

Without structured concurrency, the 'racing' logic requires complex manual cancellation and state management. With StructuredTaskScope, it becomes elegant:

public LLMResponse raceModels(List<String> modelIds, String prompt) {
    try (var scope = StructuredTaskScope.open(
            new StructuredTaskScope.Joiner.anySuccessfulResultOrThrow())) {

        for (String modelId : modelIds) {
            // Use n1n.ai unified API endpoint for routing
            scope.fork(() -> callN1NApi(modelId, prompt));
        }

        // Wait for the first success; others are automatically cancelled
        return scope.join();

    } catch (Exception e) {
        return handleFailure(e);
    }
}

In this snippet, Joiner.anySuccessfulResultOrThrow() is the key. It implements the racing pattern natively. As soon as one model (likely Haiku) returns a valid response, the scope sends an interrupt signal to the remaining threads, ensuring we don't waste compute time.

Benchmarks and Performance Analysis

I tested this router under a load of 10,000 requests with 1,000 concurrent users. The results were dramatic:

By using n1n.ai as the backend aggregator, the routing logic remained simple while the underlying Java infrastructure handled the massive concurrency without breaking a sweat.

Pro Tip: Watch out for 'Pinning'

Virtual threads are powerful, but they have a weakness called 'Pinning.' A virtual thread becomes pinned to its carrier thread if it executes a synchronized block or calls a native method (JNI). When pinned, the carrier thread cannot be released, effectively turning the virtual thread back into a platform thread.

To avoid this:

1. Replace synchronized with ReentrantLock where possible.
2. Use the flag -Djdk.tracePinnedThreads=short during development to detect and fix pinning issues.

Conclusion

Java 25's combination of Virtual Threads and Structured Concurrency represents a paradigm shift for high-performance backend development. By integrating these features with a robust LLM aggregator like n1n.ai, developers can build highly responsive, cost-effective AI applications that scale effortlessly.

Get a free API key at n1n.ai