Why Your LLM Observability Breaks on Vercel (and How to Fix It)

You've integrated an LLM observability tool into your Next.js app. Everything works perfectly in local development. You deploy to Vercel, and suddenly your dashboard shows half the events you expected. No errors. No warnings. Just... missing data.

Sound familiar? You've hit the serverless observability trap.

The Silent Failure

Most observability SDKs are designed for traditional server environments where the process stays alive indefinitely. They use fire-and-forget patterns to avoid blocking your application:

// This pattern works great in Express
app.post('/chat', async (req, res) => {
  const response = await callLLM(req.body.prompt);
  
  // Fire and forget - process stays alive, request completes
  observability.log({ input: req.body.prompt, output: response });
  
  res.json({ response });
});

The problem? Serverless functions terminate immediately after returning a response. That "fire and forget" HTTP request gets killed mid-flight.

Why This Matters for LLM Apps

LLM observability isn't optional—it's how you catch hallucinations, detect prompt injection attempts, monitor costs, and maintain compliance. Missing 30-50% of your events means:

• Incomplete risk analysis and safety monitoring
• Inaccurate cost attribution and usage metrics
• Gaps in audit trails for compliance
• Blind spots in drift detection baselines

And because the failures are silent, you might not even realize there's a problem until an auditor asks for logs you don't have.

The Fix: Await Before Return

The solution is straightforward: ensure your observability call completes before the function terminates.

// ❌ BROKEN - function terminates before request completes
export async function POST(req: Request) {
  const response = await callLLM(prompt);
  
  client.ingestAsync({  // Fire-and-forget
    model: 'gpt-4o',
    input: { prompt },
    output: { text: response }
  });
  
  return Response.json({ response });
}

// ✅ CORRECT - await ensures completion
export async function POST(req: Request) {
  const response = await callLLM(prompt);
  
  await client.ingest({  // Awaited
    model: 'gpt-4o',
    input: { prompt },
    output: { text: response }
  });
  
  return Response.json({ response });
}

Streaming Responses: The Extra Gotcha

Streaming LLM responses add another layer of complexity. You need to accumulate the full response, log it, and then close the stream:

export async function POST(req: Request) {
  const stream = new ReadableStream({
    async start(controller) {
      let fullResponse = '';
      
      for await (const chunk of llmStream) {
        fullResponse += chunk;
        controller.enqueue(chunk);
      }
      
      // ✅ Log BEFORE closing
      await client.ingest({
        model: 'gpt-4o',
        output: { text: fullResponse }
      });
      
      // Close AFTER logging
      controller.close();
    }
  });
  
  return new Response(stream);
}

If you close the stream first, the logging call may never execute—same race condition, different trigger.

Platform Compatibility

This applies to all serverless platforms:

"But Won't Awaiting Add Latency?"

Yes, but less than you'd think. A well-designed observability endpoint should respond in 50-150ms. For most LLM applications where the inference itself takes 500ms-3s, this is negligible.

If you're latency-sensitive, consider:

1. Parallel execution: Start the observability call while streaming, await at the end
2. Fail-open design: Set a timeout so observability failures don't block your response
3. Background queues: For very high-throughput apps, queue events to a durable store and process async

DriftRail's Approach

We built DriftRail with serverless as a first-class deployment target. Our SDK provides:

• ingest() - Awaitable call for serverless environments
• ingestAsync() - Fire-and-forget for long-running servers
• failOpen: true - Errors are logged but don't crash your app
• Configurable timeouts to prevent observability from blocking responses

import { DriftRail } from '@drift_rail/sdk';

const client = new DriftRail({
  apiKey: process.env.DRIFTRAIL_API_KEY,
  appId: 'my-app',
  timeout: 5000,    // 5s max
  failOpen: true    // Don't crash on errors
});

// In serverless: always await
await client.ingest({
  model: 'gpt-4o',
  provider: 'openai',
  input: { prompt },
  output: { text: response }
});

Serverless is the default deployment model for modern web applications. Your observability tooling should work with it, not against it. If you're building LLM-powered features on Vercel or Netlify, make sure your safety and monitoring infrastructure is designed for that environment.

For detailed implementation examples across all serverless platforms, see our Serverless Deployment Guide.