What Is Time to First Token (TTFT)?

What Is Time to First Token (TTFT)?

Time to first token (TTFT) is the latency from when an LLM endpoint receives a request to when it emits the first generated token. It covers tokenization, KV-cache prefill, queue time, routing, and any pre-computation the runtime does — but not output generation. TTFT scales primarily with prompt length, model size, batch state, and runtime config; for long prompts, prefill dominates. In a FutureAGI trace, TTFT is the wall-clock delta between an LLM span’s start and its first streamed-token event, captured by traceAI integrations on every call.

Why It Matters in Production LLM and Agent Systems

TTFT is what users feel as “is this thing working.” For chat, a 200ms TTFT feels snappy and a 1.5s TTFT feels unresponsive — even if the eventual streaming throughput is identical. For voice agents, TTFT is the largest contributor to time-to-first-audio (TTFA), so a slow LLM stage drags every conversational metric down with it. For agent runtimes that issue 6–12 LLM calls per user turn, TTFT compounds: a 300ms increase per call becomes 2–4 extra seconds end-to-end.

Application engineers feel TTFT regressions when a prompt-template change adds 800 tokens to every request — TTFT doubles silently because prefill scales with input length. Platform teams feel it when a new model is deployed and TTFT p99 spikes during peak hours due to provider-side queueing. SREs feel it when a routing-policy change sends traffic to a region with worse network latency. End users just say “the agent feels slow.”

For 2026 stacks the TTFT engineering tax is rising. Long-context windows mean prompts of 100k+ tokens are common; prefill at that scale eats seconds. Reasoning models like o-series add invisible “thinking time” before the first user-visible token. Multi-tool agents stack TTFT per call. FutureAGI’s role is to measure TTFT per LLM span, attribute regressions to model, prompt, route, or runtime, and tie it to quality so teams do not optimize TTFT at the cost of correctness.

How FutureAGI Handles Time to First Token

FutureAGI does not generate tokens — that is the LLM provider or self-hosted runtime (TGI, vLLM, SGLang). FutureAGI captures TTFT through traceAI integrations: traceAI-openai, traceAI-anthropic, traceAI-google-genai, traceAI-langchain, and friends instrument the SDK call and emit an LLM span with start, first-token, and end timestamps. The OpenTelemetry attribute llm.token_count.prompt rides on every span, so TTFT can be sliced by prompt length, model, route, and user cohort.

A real workflow: a coding-agent team notices TTFT p99 climb from 480ms to 1.1s after a prompt update. They open the FutureAGI trace dashboard, group LLM spans by llm.model_name and llm.token_count.prompt, and see TTFT scaling linearly with prompt length above 8k tokens — the new system prompt added 1,200 tokens of examples. They move the examples to a retrieved-context cohort with prompt caching enabled via the Agent Command Center, and TTFT p99 returns to 520ms. Pairing the latency drop with a TaskCompletion regression eval confirms no quality loss; without that pairing, the team would have shipped an unverified change.

Unlike a generic APM tool that reports HTTP latency, FutureAGI ties TTFT to per-prompt token counts and evaluator scores, so engineers see the full latency-versus-quality tradeoff in one trace.

How to Measure or Detect It

TTFT is a per-span observability signal; instrument it everywhere LLMs are called:

• TTFT p50, p90, p99 (dashboard signal): the canonical user-experience latency metric.
• llm.token_count.prompt (OTel attribute): captured on every traceAI LLM span; correlate TTFT with prompt length to spot prefill regressions.
• Per-model TTFT breakdown: TTFT varies wildly by model — measure each route separately.
• Cache-hit rate: prompt cache and KV-cache hit ratios; rising rates lower TTFT for repeated prefixes.
• Eval-fail-rate-by-cohort paired with TTFT: catches the case where a TTFT drop accompanies a quality drop.

Minimal Python:

from fi.evals import TaskCompletion
# traceAI auto-instruments your LLM client; TTFT shows up on every span.
result = TaskCompletion().evaluate(input=user_goal, trajectory=trace_spans)
print(result.score, result.reason)

Common Mistakes

• Optimizing TTFT and skipping the regression eval. A faster model usually loses 1–4 points on TaskCompletion; pair every latency win with a quality run.
• Treating TTFT and end-to-end latency as one number. They scale differently; TTFT is dominated by prefill, end-to-end by output length.
• Aggregating TTFT across all models. A 5x faster path on one model masks a regression on another; group by llm.model_name.
• Ignoring prompt-cache opportunities. Stable system prompts with long examples should be cached; pricing and TTFT both improve.
• Letting agent step count creep up. Each extra LLM call adds its own TTFT to the trajectory; cap planner depth for latency-sensitive flows.