Handling Concurrency and Rate Limits
Keep a production RAG service responsive under load with async calls, batching, retries, and backpressure.
Load in Production
A live RAG service faces many simultaneous requests, each making embedding and LLM calls. Without care you hit rate limits, time out, or exhaust memory.
Synchronous Bottleneck
Blocking on each API call serializes work. While one request waits on the LLM, the server cannot serve others, wasting capacity.
All lessons in this course
- Monitoring and Logging RAG Applications
- Caching and Performance Optimization
- Deployment Strategies for RAG in Cloud
- Handling Concurrency and Rate Limits