LLM Latency & Serving Hub
P95 latency, timeout, throughput: serving optimization playbook
This hub gathers the playbook: bottleneck analysis, streaming and perceived latency, caching, concurrency, queueing, canary and rollback, observability and tracing. For teams with P95 spikes, timeouts, or high compute cost.
Common latency & serving pains
End-to-end: retrieval → generation
Allocate latency budget per pipeline stage. Know where time goes before optimizing.
Retrieval
Vector search, BM25, reranking. Measure p50/p95 per step. Often 100–500ms depending on top-k and reranker.
Prompt assembly
Context injection, template rendering. Usually fast; can spike with large context.
Prefill (TTFT)
Time to first token. Model loading, context encoding. Dominant for long context.
Decode (generation)
Token-by-token. Throughput limited by model + hardware. Streaming improves perceived latency.
Post-processing
Citation extraction, formatting, guardrails. Can add 50–200ms.
Common bottlenecks (and how to find them)
Why LLM p95 latency spikes? Trace each step. Isolate the dominant constraint.
Retrieval slow
Symptoms: RAG pipeline timeout; retrieval dominates end-to-end.
Diagnostics: Trace retrieval step; check top-k, reranker latency, vector index size.
Fix: Reduce top-k, add caching, optimize reranker or skip for simple queries.
Context too large
Symptoms: Prefill/TTFT spikes; p95 latency blows up on long context.
Diagnostics: Measure context length distribution; correlate with latency.
Fix: Truncate, summarize, or chunk smarter. Reduce max context where possible.
Model saturation
Symptoms: Throughput plateaus; queueing; timeouts under load.
Diagnostics: GPU/utilization; queue depth; concurrent request count.
Fix: Scale replicas, batch requests, or add queue with timeout.
Cold start / model load
Symptoms: First request very slow; periodic spikes.
Diagnostics: Trace model load time; check autoscaling min replicas.
Fix: Keep warm replicas; preload models; avoid scale-to-zero for latency-sensitive paths.
Retrieval, prompt, response
Caching strategy for LLM responses and pipeline steps. Where to cache and the tradeoffs.
Retrieval cache
Cache embedding + vector results for repeated/similar queries. Semantic cache or exact match.
Prompt / context cache
KV cache or prompt caching (e.g. OpenAI). Reuse encoded context across requests.
Response cache
Cache full responses for identical queries. Simple but limited coverage.
Queueing, limits, throughput optimization
Concurrency limits, queueing behavior, and throughput optimization. Model serving infrastructure constraints.
Concurrency limits
Model serving has finite throughput. Limit concurrent requests per instance; queue the rest. Reject or timeout excess.
Queueing
Queue requests when at capacity. Set queue timeout to avoid long waits. Monitor queue depth and wait time.
Throughput optimization
Batching, continuous batching, speculative decoding. Trade latency for throughput where acceptable.
Rate limits
Protect backend from overload. Per-user, per-tenant, or global. Graceful degradation when exceeded.
Canary, rollback, rate limits
Ship new models and configs safely. Canary rollout, rollback plan, and protective limits.
Canary rollout
Route small % of traffic to new model/config. Compare latency, errors, quality. Roll back if regressions.
Rollback plan
One-click or automated rollback. Feature flags, model version routing. Document rollback triggers.
Rate limits on new releases
Throttle traffic to new version initially. Ramp up as confidence grows.
Circuit breaker
Stop sending traffic to failing instance. Fail fast; avoid cascading timeouts.
Traces, logs, SLOs
Tracing LLM pipeline end-to-end. Structured logs. SLOs and dashboards for latency and throughput.
Distributed tracing
Trace LLM pipeline end-to-end: retrieval → prefill → decode → post-process. Span per step; trace ID across services.
Structured logs
Log latency per step, token counts, model version, errors. Queryable for debugging and SLO reporting.
SLOs
p95 latency SLO, error rate SLO, availability. Alert on burn rate. Track per cohort if needed.
Dashboards
Latency by step, throughput, queue depth, cache hit rate. Correlate with releases and traffic.
Streaming vs non-streaming
Streaming improves perceived latency (TTFT matters for UX) even if total time is similar. Measure both end-to-end and time-to-first-token.
Latency pillars need before/after percentile proof
These proof assets add the missing conversion layer: trace evidence, latency budgets, and concrete serving changes that moved p95 in production.
Need latency optimization or serving audit?
We help teams diagnose P95 spikes, reduce timeouts, and optimize LLM serving infrastructure.
Start → Fix → Govern
Enforce the Audit → Sprint → Retainer ladder
Enterprise outcomes require a baseline, shipped fixes, then governance. This is the shortest path to measurable quality, controlled cost, and regression prevention.