The failure had nothing to do with the free server, and everything to do with two assumptions I had baked into my harness. I moved a small LLM batch pipeline to MonkeyCode's free server to cut costs, and within an hour the same prompts that worked on a paid endpoint started returning empty completions and hanging requests. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The real culprits were a context-window overflow and a dead keep-alive connection, and this is the retrospective of how I found both.
The Symptom
Here is the exact symptom, because vague bug reports waste everyone's time. My batch job would process about forty requests, then suddenly return a 200 response with an empty content field for the next three or four. After that, the client would hang for exactly thirty seconds before raising a ReadTimeout, and the whole run would fail on the last chunk. The strange part was that the same code had been stable for weeks on a paid endpoint, so my first instinct was to blame the new free tier.
Step 1: Write a Probe That Varies One Variable
I forced myself to stop guessing and wrote a minimal probe that isolates the variable I suspected most: input length. The script sends the same trivial prompt, padded with filler characters, and records status, latency, and whether the expected token appears in the reply. Run it against any OpenAI-compatible endpoint, and you get a table that separates a sick server from a sick request.
