A Load-Testing Playbook for Free AI Servers (and Why I Wrote One)

Free AI servers often seem too good to be true, but they can be unreliable for production use. These services promise free usage with minimal setup but may falter under even moderate traffic. I experienced this issue firsthand and created a simple load testing tool to verify the stability of a free tier before committing to it. Below is the straightforward script that performs this task.

The load tester operates by simultaneously sending a set number of HTTP requests to a health endpoint. It records each response's status code and latency, allowing you to assess the server's performance objectively. This script utilizes the asyncio and aiohttp libraries for efficient concurrency management, avoiding the performance hit that would come from spawning a thread for each request.

To use the script, save it as loadtest.py and ensure aiohttp is installed via pip install aiohttp. Then, run the script with the health endpoint URL, the desired concurrency level, and the total number of requests you wish to send. The output provides an overview of the test's success rate, average and maximum latency, and any encountered error types.

For accurate results, it's recommended to incrementally increase the number of concurrent requests from a low baseline to a higher level, allowing you to observe how the server behaves under varying loads. This incremental testing approach helps you gauge whether the free tier can handle your anticipated traffic without compromising service quality.