LLM apps need queues, caching, and sanity checks to survive async generation APIs
Defiant_Dentist5191 · reddit · 2026-07-23
How teams handle async generation APIs, rate limits, and silent degradation
The post asks how people operate third-party generation APIs inside LLM apps when the APIs are async, quota-limited, and occasionally return degraded output without a hard failure.
Pain points mentioned:
- Async jobs: polling is simple but wasteful; callbacks are cleaner but require public endpoints and more failure handling
- Credit ceilings: generation-based limits can be surprisingly low, forcing architectural choices like caching and request deduplication
- Soft failures: the worst case is a 200 OK response that is slower or lower quality than usual
The author’s current pattern is:
- A small queue in front of every external generation call
- Aggressive caching keyed by input hash
- A capped polling loop with fallback routing
- An output sanity check instead of trusting status codes alone
The discussion is really about production architecture for external AI services at volume.
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