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End-to-End Response Time

Benched.ai Editorial Team

End-to-end response time (E2E RT) is the wall-clock duration from the moment a user submits a request until the client receives the last byte of the response.

  Latency Component Breakdown

ComponentTypical ShareOptimization
TLS handshake5–20 msConnection reuse, HTTP/3
Network transit20–120 msEdge POPs, QUIC congestion control
Queue & batch wait0–200 msAutoscaling, priority lanes
Inference compute50–1500 msModel size, quantization, caching
Post-processing5–50 msJSON schema, hallucination filter
Client render5–30 msVirtual DOM diffing

  Target Budgets (chat, 1k output tokens)

SLA TierP50P95Comment
Gold1.5 s3.0 sPremium latency sku
Silver2.5 s5.0 sDefault
Bronze5.0 s10 sCost-optimized

  Design Trade-offs

  • Larger batches lower cost per token but raise queue delay.
  • Streaming halves perceived latency but increases socket overhead.
  • Edge inference reduces transit time yet limits GPU choice.

  Current Trends (2025)

  • Kernel fusion and speculative decoding shrink compute latency 30-50 %.
  • gRPC over HTTP/3 reduces head-of-line blocking at tail latencies.
  • Client SDKs pre-allocate chat bubbles based on token rate predictions.

  Implementation Tips

  1. Use distributed tracing to attribute latency per segment.
  2. Alert on p95 not average—users notice tail delays.
  3. Budget network and compute separately when negotiating provider SLOs.