api-experiments

To prevent memory bloat during large-scale LLM evaluations, submit all configurations from a single process, poll for completion, and retrieve results incrementally rather than spawning parallel processes.

The best way to manage concurrency for batch API experiments is to use a single-process submission model that polls the API and collects results incrementally, avoiding the parallel-process anti-pattern.

Your LLM experiment pipeline likely crashes during multi-config sweeps because of the parallel-process anti-pattern, which causes memory bloat and process explosions; enforcing single-process submission and polling resolves this.

Yes, you can use asynchronous workflows with controlled concurrency for iterative LLM API testing, which supports interactive iterations while maintaining memory efficiency and preventing process explosions.

You need to enforce single-process submission for API-heavy experiments when running large eval campaigns or multi-config sweeps, ensuring incremental result retrieval and safeguarding against memory bloat from parallel processes.