What is TTFT?
TTFT is the latency from sending an LLM request until the first generated token is received by the client.
Quick Facts
| Full Name | Time to First Token |
|---|
How It Works
TTFT stands for Time to First Token. It is one of the most important user-perceived latency metrics for streaming LLM products because it determines how quickly the interface feels responsive. TTFT includes more than model compute: request routing, queueing, safety checks, prompt tokenization, prefill, the first decode step, and network delay can all contribute. Optimizing TTFT often requires shortening prompts, reducing queue time, caching common context, and separating long-context workloads from latency-sensitive chat.
Key Characteristics
- Measures startup latency before the first streamed token appears
- Strongly affected by prefill cost and input token length
- Includes serving overhead such as queueing, routing, and network latency
- More important for interactive chat than for offline batch generation
- Should be tracked separately from total latency and tokens per second
Common Use Cases
- Monitoring perceived responsiveness of an AI chat product
- Comparing long-context prompts against short prompts
- Detecting serving queue saturation during traffic spikes
- Evaluating benefits from context caching
- Setting latency SLOs for streaming LLM APIs
Example
Loading code...Frequently Asked Questions
Is TTFT the same as total response latency?
No. TTFT measures when the first token arrives, while total latency measures when the full response completes.
Why does long context increase TTFT?
The model must run prefill over all input tokens before producing the first generated token.
What is a good TTFT?
It depends on product expectations, but interactive chat usually needs low TTFT so users see progress quickly.
How can TTFT be improved?
Reduce input tokens, lower queueing delay, cache shared context, optimize routing, and use serving engines tuned for prefill.