Does Using an LLM Aggregator Increase Latency?
An LLM aggregator can add a small latency overhead, but routing and provider distance usually matter more. How to benchmark p50 and p95 against direct calls.
Overview
Using an LLM aggregator can increase latency slightly, but the real impact depends on routing, provider distance, request processing, model choice, network conditions, and whether the aggregator adds useful fallback or observability. The only reliable answer comes from testing your own workload.
- Measure time to first token, total response time, and error rate.
- Compare direct provider calls with aggregator calls using the same prompts.
- Check whether routing, retries, caching, or fallback offsets any added delay.
Latency should be evaluated against product needs. A background content job can tolerate more delay than a real-time support chat. A routing layer may add overhead, but it may also reduce downtime by giving teams alternative providers when one route fails.
For WaveSpeedAI users, the transparent answer is that aggregation is not magic; it is a trade-off between simplicity, flexibility, and performance. For production teams, the right test is to measure direct versus aggregated calls under realistic traffic and decide whether the operational benefits justify any latency difference. Measure aggregator latency yourself: benchmark p50 and p95 for the same model called directly and through the layer, from your production region; accept the overhead only if the operational gains outweigh it.
