RAG End-to-End Latency Budgeting: Where the Milliseconds Go

RAG pipelines have multiple stages that add up quickly.

Without budgets, teams optimize the wrong components.

Measure retrieval, reranking, prompt assembly, and generation separately.

Pinpoint which stage dominates p99 latency.

Set targets for each stage based on business goals.

Use smaller models or caches where budgets are tight.

Use distributed tracing to capture latency across services.

Surface budgets in dashboards so teams can see drift.

Start with retrieval caching and reranker batching.

Then tune prompt size and model routing.

Set hard ceilings per stage to prevent runaway latency spikes.

Drop or simplify low-priority stages when budgets are exceeded.

Expose budget status in logs so teams see which stage violated targets.

Use progressive timeouts to protect p99 under heavy load.

Define fallback paths when rerankers or retrievers time out.

Track budget violations per route to guide targeted fixes.

Align budgets with user tiers so premium traffic stays protected.

Review budget exceptions weekly to prevent silent drift.

Add feature flags to disable costly stages during incidents.

Measure queueing time separately to avoid masking upstream delays.

Set per-tenant caps to prevent noisy neighbors from exhausting budgets.

Track budget compliance by region to spot localized bottlenecks.

Forecast latency impact under peak traffic and large prompts.

Model retriever and reranker capacity separately from generation.

Use load tests with realistic query mixes, not synthetic averages.

Measure cold start penalties for autoscaled components.

Pre-warm caches before known traffic events to avoid spikes.

Maintain headroom for traffic bursts and upstream dependencies.

Track utilization to decide where to add capacity first.

Document capacity assumptions so budgets stay realistic.

Budget for worst-case document sizes to avoid surprise slowdowns.

Plan for regional failover so capacity shifts are smooth.

What is the biggest bottleneck? Often retrieval and reranking.

Can I skip reranking? Only if precision remains acceptable.

What is the fastest win? Cache top queries and reuse embeddings.