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Fix 504 Gateway Timeout on AI Workloads
Architecture recipe to decouple long-running LLM loops into background task queues.
When synchronous HTTP requests attempt to execute multi-agent reasoning, deep scraping, or embedding generation, Nginx and Cloudflare proxies terminate the connection. Learn how to implement the Asynchronous Queue Architecture.
Standard web servers (like Next.js API routes or Express handlers) are designed for microsecond response times. When an endpoint calls an AI agent that takes 45 seconds:
Instead of holding the HTTP connection open, structure your API to dispatch the job to a Redis worker queue:
// Step 1: Endpoint dispatches job & returns 202 immediately
app.post('/api/run-ai-agent', async (req, res) => {
const job = await aiQueue.add('analyze', req.body);
return res.status(202).json({ success: true, jobId: job.id });
});
// Step 2: Dedicated Worker Node processes in background
const worker = new Worker('aiQueue', async (job) => {
return await executeComplexAiPipeline(job.data);
}, { connection: redisConfig });
Increasing `proxy_read_timeout 300s` is a temporary bandage. If multiple users execute AI queries simultaneously, server connection pools will exhaust, crashing the entire web server. The async queue pattern is the only scalable architectural fix.
Scalable 1–8 vCPUs, 4–32 GB RAM, and NVMe Storage. Dedicated environment for BullMQ and Redis workers.
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