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Routing and reliabilityBrief

LLM latency optimization: improve time to first useful answer

Average duration hides the experience. Measure queueing, time to first token, generation rate, tools, and retries—then optimize the stage users actually feel.

By INFRO Editorial

Published

Updated

1 min read

A fast glowing AI request racing through a low-latency infrastructure path with precise timing signals

In short

LLM latency optimization starts by separating queue time, connection time, time to first token, generation time, tools, and retries. Improve the metric tied to the experience—often first useful output for chat and total completion time for batch work.

On this page
  1. Measure the latency budget
  2. Reduce work before changing models
  3. Choose and route for the experience
  4. Control retries and fallbacks

Measure the latency budget

Record timestamps at the application, gateway, provider attempt, first byte, first token, tool boundary, and completion. Use p50, p95, and p99 by model, route, region, and workload; averages hide the slow tail users remember.

A recovered request can be successful and still violate the latency objective. Keep attempt and fallback time visible.

Reduce work before changing models

Shorten unnecessary context, retrieve fewer higher-quality chunks, cap output length, stream early, parallelize independent retrieval, and cache deterministic work. For agents, reduce serial tool calls and persist state instead of repeatedly reconstructing it.

Do not trim instructions that protect correctness just to win a benchmark. The useful metric is time to a correct answer.

Choose and route for the experience

Interactive chat may favor fast first-token behavior; classification favors total duration; voice needs predictable streaming; batch generation may favor throughput and cost. Evaluate routes against the workload's actual objective.

Use recent route health and latency, not a permanent ranking. Provider performance changes by region, model, load, and time.

Control retries and fallbacks

Set per-attempt and total deadlines. A long first timeout followed by a successful fallback creates a slow success that may be worse than a fast degraded response.

Hedge requests only for carefully selected high-value traffic because parallel attempts can multiply cost and provider load.

From the team that builds it

Where this shows up in INFRO

INFRO is a control plane in front of the provider accounts you already hold. The capabilities below are the parts of it this article touches, each with the documentation that defines its behaviour.

INFRO accounts are self-serve. Provider usage is billed to you by your providers; INFRO charges a software subscription and takes 0% of model spend. See plans.

About this article

Last checked against its sources on . The next review is due by . No separate named reviewer is recorded for this article.

How articles are written, sourced and corrected is set out in the editorial standards.

Revision history

  1. Removed the promotional template sections and the generic FAQ, moved product context into the product section, and corrected product wording to the current BYOK model.

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