What is TTFB by LLM Engine
TTFB by LLM Engine shows how large language model (LLM) services affect Time to First Byte (TTFB) — the time it takes for the server to start responding to a user’s request.
This lens helps you identify whether AI services running on the backend — such as content generation, personalization, or decision-making logic — are delaying the server response.
Only LLM engines that were active and measurable during the selected period are listed.
Why it matters: LLMs sometimes power real-time decisions or page generation. If they delay server-side processing, users wait longer just to begin loading the page — often without knowing why.
Healthy TTFB by LLM Engine sample
Should you worry
A healthy setup shows fast server responses even when LLM engines are in use.
That means:
AI decisions are cached, deferred, or handled post-response.
Server routes return quickly and aren't held up waiting on LLM output.
Fallbacks or timeouts are in place to prevent long delays.
If your TTFB values are green regardless of AI involvement, your architecture is AI-ready without compromising speed.
Unhealthy TTFB by LLM Engine sample
When LLM engines slow down TTFB, it’s usually because:
Pages rely on real-time AI content generation before sending HTML.
Server logic waits for a personalized AI recommendation or block.
LLM APIs are slow, overloaded, or inconsistently cached.
Third-party AI calls block your server’s first byte response.
Common scenarios include:
AI-powered article generation or summaries rendered server-side.
Pages that personalize layout or content at runtime based on AI output.
Delays in headless CMS or app server waiting on LLM decision trees.
If these delays are tied to specific engines, users may see a blank screen for several seconds before anything loads.
Resolving unhealthy TTFB by LLM Engine
Go-to action plan to resolve an unhealthy TTFB by LLM Engine:
Ask Uxi to analyze your TTFB by LLM Engine values and suggest improvements.
Use Filters to narrow the issue by page type, route, or user segment.
Simulate TTFB of the suspected lens to see if fixing it will resolve the TTFB by LLM Engine. If yes, this is where the resolution focus should be.
Use an automated optimization tool like Navigation AI to improve your TTFB by LLM Engine values.
Once you’ve improved TTFB, set an alert to be the first to know if it starts worsening again.
Try it yourself
Discover how your website performs with real user data.