Latency monitoring: Tracking LLM response times

Speed is the first thing users notice. Slow replies break focus and trust within seconds.

LLM products live and die by that first token. If the app streams something quickly, users lean in. If it stalls, they bounce and the rest of the quality never gets seen.

Jump to: Why latency matters | Key metrics | Tactics | Tags and tracing

Why latency matters for LLM usage

Fast responses keep attention; slow ones break flow. Time to first token sets trust. Mobile round trips stack up quickly, which Martin Kleppmann explained years ago in his breakdown of mobile RTTs and TCP handshakes here. On weak networks, that cost can dominate everything else.

Interactive features need snappy turn-taking. A meeting assistant that waits 10 seconds is dead on arrival; sub second feels live, which is why real-time builders push so hard on it in threads like this Reddit discussion. You win intent in those first seconds.

Speed also shapes the product you get to ship. High delay blocks multi-step agents, long tool chains, and voice loops. Low delay unlocks progressive token streams and tight feedback loops. Live media teams make similar tradeoffs when they chase scale without ruining experience, as covered by Pragmatic Engineer in their streaming write-up here. In practice, shorter, more direct outputs help more than shaving a word from the prompt.

None of this works without visibility. You need AI observability tied to user experience, not just servers and GPUs. Track token pace, first-token delay, errors, and prompt versions with trace context, as teams share in this monitoring thread on r/MachineLearning here and in Statsig's work on experiment-aware observability with Datadog here. Then experiment your way into a faster UX, using users as the benchmark rather than guesses, a point Statsig stresses in their guide to testing and optimization here.

Key metrics behind response time measurement

Start with three metrics that match how users feel speed. These anchor your SLAs and your experiments.

• Time to first token (TTFT): how fast visible output starts.

• Time per output token (TPOT): how fast tokens stream after the first.

• Aggregate generation time: TTFT plus output pace across the response.

TTFT sets perceived snappiness. Network round trips and queue depth often dominate it, especially on mobile. Kleppmann's RTT analysis is still a great explainer of why the first byte is hard on phones here. Early token delivery makes products feel alive.

TPOT governs reading flow and comprehension. Faster tokens keep scanning smooth and reduce abandonment. Builders chasing real-time goals in meeting scenarios say the same thing, as seen in this thread here. Token pace can be quantified with local rigs and hosted models, as discussed by the LocalLLaMA community here.

Aggregate generation time shows end-to-end speed and cost tradeoffs. Tag responses with user, feature, and prompt versions so you can slice results by experience, not only by server. Community tools and posts show how to wire this up, from monitoring agents in production here to open-source tracing projects here. For optimization, marry these metrics to controlled tests, as covered in Statsig's experiment playbook for LLMs here.

Quick SLA checklist:

• TTFT p50 and p95 for each surface and device type

• TPOT tokens per second during the first 2 seconds of streaming

• End-to-end p95 with cost per response, tagged to feature and prompt version

Tactics for lowering overall LLM latency

You measured TTFT and TPOT. Now fix them with targeted changes and AI observability.

Trim TTFT:

• Reduce network hops and handshakes. Collocate API, router, and model host; keep connections warm. Kleppmann's mobile RTT breakdown shows why every extra round trip hurts here.

• Start streaming immediately. Prioritize the first tokens so the UI moves while heavier work continues.

• Cache or precompute where possible. Prebuild embeddings or tool metadata that would otherwise block the first token.

Accelerate TPOT and perceived pace:

• Stream responses by default. Live video teams take the same approach to keep viewers engaged at scale Pragmatic Engineer. Real-time builders do it for voice and meetings too Reddit.

• Control output length. Short answers cut TPOT and cost. Use experiment data to set length rules, then verify the drop in dashboards, as shown in Statsig's guides here and here.

• Right-size the model and batching strategy. Pick a model size and batch setting that holds p95 steady under load.

Boost throughput without blowing up tails:

• Size batches to your model and GPU; watch p95 latency under real traffic. Practical notes from the LLMDevs community are here link.

• Favor simple, single-writer hot paths when possible. The LMAX architecture shows how a single-threaded, lock-free path can stay fast under pressure Martin Fowler.

• Validate with load tools and latency histograms. ApacheBench is still a handy way to push endpoints and compare settings Kleppmann.

Two habits that compound:

• Track per-feature latency, tokens, and cost with AI observability, and alert on regressions across experiments and prompt versions r/MachineLearning open-source tracing Statsig guide.

• Treat maximum output length and temperature as product levers. Tune them with online tests, not gut feel Statsig experiment playbook.

Tags, tracing, and real-time monitoring

Instrumentation starts with rich tags. Think user_id, feature_name, request_id, and prompt_version. Add token counts, TTFT, TPOT, and final latency. This fuels AI observability and fast root cause analysis.

Tie distributed traces to users and features so you do not lose the plot across services. Map spans from API to model calls and tools, then attribute queuing and network time. Teams share practical approaches in r/MachineLearning threads about monitoring agents here. Principles from the LMAX write-up help isolate hot paths you should keep simple Martin Fowler.

Set SLOs on TTFT, TPOT, and p95 end-to-end latency. Alert on breaches with context: tags, spans, and costs. For a view that marries experiments and observability, see Statsig's perspective on Datadog integration here.

Practical tags you can apply now:

• session_id; model_name; prompt_version; dataset_id from tests in LLM optimization

• channel; device_type; network_type, which matter a lot on mobile per Kleppmann's note on RTT why the mobile web is slow

• workload_class; batch_id; node_id, which help analyze throughput as discussed in LocalLLaMA threads here

• realtime flags for sub second goals, as in this meeting bot thread here

Small roadmap to get there:

1. Instrument TTFT and TPOT, start streaming in the UI.

2. Add tracing that links spans to user and feature.

3. Layer in experiment-aware dashboards so you can ship improvements with confidence Statsig + Datadog.

Closing thoughts

Latency is not just infra; speed is a product decision. Ship the first token fast, keep tokens flowing, and control output length. Measure TTFT and TPOT, then use AI observability and experiments to chase real improvements, not vibes. The teams that win do this relentlessly.

More to explore:

• Statsig's guide to testing and optimizing AI with online experiments here

• LLM optimization with online experimentation here

• Experiment-aware observability with Datadog here

Hope you find this useful!