LLM App Load Patterns

I used to think load testing was just “push harder and see what breaks.”

Then I watched a system look great under a neat, constant stream of traffic.

And fall apart the moment requests arrived the way real users actually behave: unevenly, unpredictably, in bursts.

The lesson: if you only test one traffic pattern, you are not measuring production readiness — you are measuring one very specific condition.

That is why I pay attention to load patterns like 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 in LLM app benchmarking.

A Poisson pattern sends requests asynchronously with random spacing, while still respecting an average rate.

In practice, that matters because:

• real users do not arrive like clockwork
• queueing effects show up differently under bursty traffic
• latency can look fine in steady state and still degrade badly under randomness
• capacity planning becomes much more honest

For LLM systems, this is especially important because the bottlenecks are rarely just “the model.”
They can be the serving engine, batching behavior, hardware, quantization, or the way your app handles concurrency.

So when I evaluate an AI system, I want more than a clean benchmark number.
I want to know how it behaves when traffic looks like production.

What traffic pattern do you trust most when you benchmark LLM apps?