Peak traffic causing slowdown in AI responses

Peak traffic exposes every weak point in an AI stack. A flow that feels fast during normal load can suddenly slow down when concurrency rises, because retrieval, model inference, and downstream services all compete for resources at once.

Users do not care why the slowdown happened. They just feel the delay and assume the product is unreliable. That is why peak-time performance matters as much as average latency.

Handling this well usually means routing simpler requests faster, precomputing where possible, reducing context size, and adding enough infrastructure headroom to survive demand spikes without falling apart.