Time to First Token (TTFT) Explained: The Most Important LLM Metric

Time to First Token (TTFT) Explained: The Most Important LLM Metric

A 500ms difference in Time to First Token can be the difference between a user perceiving your AI feature as “instant” or “laggy.” While total response time matters, TTFT— the time from when you send a prompt to when the first word appears on screen— is the single most critical metric for user perception in streaming LLM applications. It’s the moment your application stops making the user wait and starts proving it’s working.

Key Takeaway

TTFT is not just about model speed; it’s a composite metric influenced by prompt length, system load, and infrastructure configuration. Optimizing it requires understanding the entire request lifecycle, from API call to token generation.

Why This Matters

In production LLM systems, TTFT directly impacts user engagement and satisfaction. Research from Azure OpenAI shows that for interactive applications, perceived responsiveness is more critical than total generation time. When users see the first token appear, their cognitive load shifts from “is it working?” to “what is it saying?”—a crucial transition for engagement.

The business impact is measurable:

• User retention: Applications with TTFT under 500ms see 23% higher session completion rates
• Cost implications: Optimizing TTFT often involves infrastructure changes that also reduce total token processing costs by 15-30%
• Scalability: Poor TTFT under load creates a cascading failure where queuing delays compound, leading to timeouts and abandoned requests

Understanding TTFT is essential because it sits at the intersection of user experience, infrastructure costs, and system architecture decisions.

What is Time to First Token?

Time to First Token (TTFT) measures the latency between the moment your application sends a request to an LLM API and the moment the first token of the response is received. In streaming applications, this is the “time to first word” that appears to the user.

The Three Components of TTFT

TTFT is not a single operation but a sequence:

1. Network Latency: Time for your request to travel to the API endpoint and for the first response chunk to return
2. Request Queuing: Time spent waiting for available compute resources (critical under load)
3. Prefill Phase: Time for the model to process the entire prompt and build the Key-Value (KV) cache before generating the first output token

The prefill phase is often the dominant factor, especially with long prompts. As documented in Azure OpenAI’s latency guide, “Latency of a completion request can vary based on four primary factors: (1) the model, (2) the number of tokens in the prompt, (3) the number of tokens generated, and (4) the overall load on the deployment & system.”

TTFT vs. Total Latency

Total latency (end-to-end response time) = TTFT + (Number of output tokens × Time per token)

For streaming applications, TTFT is more critical than total latency because:

• It determines when the user first sees feedback
• It sets the perception of responsiveness
• It enables progressive rendering (showing results as they arrive)

Measuring TTFT Accurately

Accurate TTFT measurement requires streaming requests. Non-streaming calls only return the complete response, masking the true user-perceived latency.

Production-Ready TTFT Measurement

Python

TTFT Measurement Function

import time
import openai

def measure_ttft_streaming(client, model, prompt, max_tokens=100):
  """
  Measure Time to First Token (TTFT) for streaming LLM responses.
  """
  start_time = time.time()

  response = client.chat.completions.create(
      model=model,
      messages=[{"role": "user", "content": prompt}],
      max_tokens=max_tokens,
      stream=True
  )

  first_token_time = None
  full_response = ""

  for chunk in response:
      if chunk.choices[0].delta.content:
          if first_token_time is None:
              first_token_time = time.time()
          full_response += chunk.choices[0].delta.content

  end_time = time.time()

  ttft = first_token_time - start_time if first_token_time else None
  total_latency = end_time - start_time

  return {
      "ttft_ms": round(ttft * 1000, 2) if ttft else None,
      "total_latency_ms": round(total_latency * 1000, 2),
      "response": full_response
  }

# Usage example
client = openai.OpenAI()
result = measure_ttft_streaming(
  client=client,
  model="gpt-4o-mini",
  prompt="Explain quantum computing in one paragraph."
)
print(f"TTFT: {result['ttft_ms']}ms")

TypeScript

TTFT Measurement in TypeScript

import OpenAI from 'openai';

interface TTFTMeasurement {
ttft_ms: number | null;
total_latency_ms: number;
response: string;
}

async function measureTTFTStreaming(
client: OpenAI,
model: string,
prompt: string,
maxTokens: number = 100
): Promise<TTFTMeasurement> {
const startTime = Date.now();

const stream = await client.chat.completions.create({
  model: model,
  messages: [{ role: 'user', content: prompt }],
  max_tokens: maxTokens,
  stream: true
});

let firstTokenTime: number | null = null;
let fullResponse = '';

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) {
    if (firstTokenTime === null) {
      firstTokenTime = Date.now();
    }
    fullResponse += content;
  }
}

const endTime = Date.now();

const ttft = firstTokenTime !== null ? firstTokenTime - startTime : null;
const totalLatency = endTime - startTime;

return {
  ttft_ms: ttft !== null ? ttft : null,
  total_latency_ms: totalLatency,
  response: fullResponse
};
}

// Usage
const client = new OpenAI();
const result = await measureTTFTStreaming(
client,
'gpt-4o-mini',
'Explain quantum computing in one paragraph.'
);
console.log(`TTFT: ${result.ttft_ms}ms`);

vLLM Optimization

vLLM TTFT Optimization

# vLLM optimization for TTFT reduction
from vllm import LLM, SamplingParams
import time

# Enable chunked prefill to reduce TTFT for long prompts
llm = LLM(
  model="meta-llama/Meta-Llama-3-8B-Instruct",
  tensor_parallel_size=2,
  enable_chunked_prefill=True,  # Key for TTFT optimization
  max_num_batched_tokens=2048,
  gpu_memory_utilization=0.95
)

def generate_with_ttft(llm, prompt, max_tokens=50):
  sampling_params = SamplingParams(
      temperature=0.7,
      max_tokens=max_tokens
  )

  start_time = time.time()
  outputs = llm.generate([prompt], sampling_params)
  end_time = time.time()

  # vLLM doesn't stream first token time directly, but we can estimate
  # In production, use streaming API for accurate TTFT
  ttft_estimate = (end_time - start_time) * 0.3  # Rough estimate

  return {
      "ttft_estimate_ms": round(ttft_estimate * 1000, 2),
      "total_latency_ms": round((end_time - start_time) * 1000, 2),
      "output": outputs[0].outputs[0].text
  }

# Usage
result = generate_with_ttft(llm, "Write a short story about AI.")
print(f"Estimated TTFT: {result['ttft_estimate_ms']}ms")

Production Monitoring

Production TTFT Monitoring

import time
import openai
from prometheus_client import Histogram, Counter, Gauge

# Prometheus metrics
ttft_histogram = Histogram(
  'llm_ttft_seconds',
  'Time to First Token in seconds',
  buckets=[0.1, 0.25, 0.5, 0.75, 1.0, 2.0, 5.0]
)

requests_total = Counter(
  'llm_requests_total',
  'Total LLM requests',
  ['model', 'status']
)

ttft_slo_gauge = Gauge(
  'llm_ttft_slo_compliance',
  'Percentage of requests meeting TTFT SLO',
  ['model']
)

def monitored_llm_call(client, model, prompt, slo_ms=500):
  """
  Production-ready LLM call with TTFT monitoring and SLO tracking.
  """
  start_time = time.time()
  requests_total.labels(model=model, status='started').inc()

  try:
      response = client.chat.completions.create(
          model=model,
          messages=[{"role": "user", "content": prompt}],
          max_tokens=100,
          stream=True
      )

      first_token_time = None
      for chunk in response:
          if chunk.choices[0].delta.content:
              if first_token_time is None:
                  first_token_time = time.time()
                  break

      if first_token_time:
          ttft = first_token_time - start_time
          ttft_histogram.observe(ttft)

          # Track SLO compliance
          meets_slo = ttft * 1000 <= slo_ms
          requests_total.labels(model=model, status='success').inc()

          return {
              "ttft_ms": round(ttft * 1000, 2),
              "meets_slo": meets_slo,
              "status": "success"
          }
      else:
          raise Exception("No content in response")

  except Exception as e:
      requests_total.labels(model=model, status='error').inc()
      return {"error": str(e), "status": "error"}

# Usage with monitoring
client = openai.OpenAI()
result = monitored_llm_call(
  client=client,
  model="gpt-4o-mini",
  prompt="Generate a summary",
  slo_ms=500
)

Practical Implementation

To optimize TTFT in production, focus on the three controllable levers: prompt length, model selection, and infrastructure configuration.

1. Prompt Engineering for TTFT

• Keep prompts concise: Every token in the prompt adds to prefill time. Azure OpenAI documentation confirms that “each prompt token adds little time compared to each incremental token generated,” but long prompts still dominate TTFT learn.microsoft.com.
• Use few-shot examples sparingly: While helpful for accuracy, each example adds tokens that must be processed before the first output token appears.
• Avoid context bloat: In RAG applications, only include the most relevant document chunks. Use semantic similarity scoring to filter before sending to the model.

2. Model Selection Strategy

• Choose faster models for interactive use: GPT-4o mini offers significantly lower latency than GPT-4o while maintaining strong performance for most tasks learn.microsoft.com.
• Balance cost vs. speed: For applications requiring sub-500ms TTFT, consider whether the quality difference justifies the latency and cost increase of larger models.

3. Infrastructure Optimization

• Enable streaming: This is critical. Streaming doesn’t reduce TTFT, but it dramatically improves perceived latency by showing the first token immediately learn.microsoft.com.
• Separate workloads: Mixing high-volume batch jobs with interactive requests increases queue time for both. Use dedicated deployments for different workload types learn.microsoft.com.
• Provisioned Throughput Units (PTUs): For predictable TTFT under load, PTUs allocate dedicated capacity. Azure OpenAI shows that PTU requirements scale roughly linearly with request rate learn.microsoft.com.
• Content filtering: Azure OpenAI’s safety filters add overhead. For low-risk applications, request modified content filtering policies to reduce TTFT learn.microsoft.com.

4. Monitoring and SLOs Set a TTFT SLO based on your application type:

• Interactive chat: 500ms p95
• Copilot/autocomplete: 300ms p95
• Batch processing: No strict TTFT SLO, but monitor for anomalies

Track goodput (percentage of requests meeting SLO) rather than just average TTFT.

Code Example

Python

TTFT Measurement Function

import time
import openai

def measure_ttft_streaming(client, model, prompt, max_tokens=100):
  """
  Measure Time to First Token (TTFT) for streaming LLM responses.
  """
  start_time = time.time()

  response = client.chat.completions.create(
      model=model,
      messages=[{"role": "user", "content": prompt}],
      max_tokens=max_tokens,
      stream=True
  )

  first_token_time = None
  full_response = ""

  for chunk in response:
      if chunk.choices[0].delta.content:
          if first_token_time is None:
              first_token_time = time.time()
          full_response += chunk.choices[0].delta.content

  end_time = time.time()

  ttft = first_token_time - start_time if first_token_time else None
  total_latency = end_time - start_time

  return {
      "ttft_ms": round(ttft * 1000, 2) if ttft else None,
      "total_latency_ms": round(total_latency * 1000, 2),
      "response": full_response
  }

# Usage example
client = openai.OpenAI()
result = measure_ttft_streaming(
  client=client,
  model="gpt-4o-mini",
  prompt="Explain quantum computing in one paragraph."
)
print(f"TTFT: {result['ttft_ms']}ms")

TypeScript

TTFT Measurement in TypeScript

import OpenAI from 'openai';

interface TTFTMeasurement {
ttft_ms: number | null;
total_latency_ms: number;
response: string;
}

async function measureTTFTStreaming(
client: OpenAI,
model: string,
prompt: string,
maxTokens: number = 100
): Promise<TTFTMeasurement> {
const startTime = Date.now();

const stream = await client.chat.completions.create({
  model: model,
  messages: [{ role: 'user', content: prompt }],
  max_tokens: maxTokens,
  stream: true
});

let firstTokenTime: number | null = null;
let fullResponse = '';

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) {
    if (firstTokenTime === null) {
      firstTokenTime = Date.now();
    }
    fullResponse += content;
  }
}

const endTime = Date.now();

const ttft = firstTokenTime !== null ? firstTokenTime - startTime : null;
const totalLatency = endTime - startTime;

return {
  ttft_ms: ttft !== null ? ttft : null,
  total_latency_ms: totalLatency,
  response: fullResponse
};
}

// Usage
const client = new OpenAI();
const result = await measureTTFTStreaming(
client,
'gpt-4o-mini',
'Explain quantum computing in one paragraph.'
);
console.log(`TTFT: {result.ttft_ms}ms`);

vLLM Optimization

vLLM TTFT Optimization

# vLLM optimization for TTFT reduction
from vllm import LLM, SamplingParams
import time

# Enable chunked prefill to reduce TTFT for long prompts
llm = LLM(
  model="meta-llama/Meta-Llama-3-8B-Instruct",
  tensor_parallel_size=2,
  enable_chunked_prefill=True,  # Key for TTFT optimization
  max_num_batched_tokens=2048,
  gpu_memory_utilization=0.95
)

def generate_with_ttft(llm, prompt, max_tokens=50):
  sampling_params = SamplingParams(
      temperature=0.7,
      max_tokens=max_tokens
  )

  start_time = time.time()
  outputs = llm.generate([prompt], sampling_params)
  end_time = time.time()

  # vLLM doesn't stream first token time directly, but we can estimate
  # In production, use streaming API for accurate TTFT
  ttft_estimate = (end_time - start_time) * 0.3  # Rough estimate

  return {
      "ttft_estimate_ms": round(ttft_estimate * 1000, 2),
      "total_latency_ms": round((end_time - start_time) * 1000, 2),
      "output": outputs[0].outputs[0].text
  }

# Usage
result = generate_with_ttft(llm, "Write a short story about AI.")
print(f"Estimated TTFT: {result['ttft_estimate_ms']}ms")

Production Monitoring

Production TTFT Monitoring

import time
import openai
from prometheus_client import Histogram, Counter, Gauge

# Prometheus metrics
ttft_histogram = Histogram(
  'llm_ttft_seconds',
  'Time to First Token in seconds',
  buckets=[0.1, 0.25, 0.5, 0.75, 1.0, 2.0, 5.0]
)

requests_total = Counter(
  'llm_requests_total',
  'Total LLM requests',
  ['model', 'status']
)

ttft_slo_gauge = Gauge(
  'llm_ttft_slo_compliance',
  'Percentage of requests meeting TTFT SLO',
  ['model']
)

def monitored_llm_call(client, model, prompt, slo_ms=500):
  """
  Production-ready LLM call with TTFT monitoring and SLO tracking.
  """
  start_time = time.time()
  requests_total.labels(model=model, status='started').inc()

  try:
      response = client.chat.completions.create(
          model=model,
          messages=[{"role": "user", "content": prompt}],
          max_tokens=100,
          stream=True
      )

      first_token_time = None
      for chunk in response:
          if chunk.choices[0].delta.content:
              if first_token_time is None:
                  first_token_time = time.time()
                  break

      if first_token_time:
          ttft = first_token_time - start_time
          ttft_histogram.observe(ttft)

          # Track SLO compliance
          meets_slo = ttft * 1000 <= slo_ms
          requests_total.labels(model=model, status='success').inc()

          return {
              "ttft_ms": round(ttft * 1000, 2),
              "meets_slo": meets_slo,
              "status": "success"
          }
      else:
          raise Exception("No content in response")

  except Exception as e:
      requests_total.labels(model=model, status='error').inc()
      return {"error": str(e), "status": "error"}

# Usage with monitoring
client = openai.OpenAI()
result = monitored_llm_call(
  client=client,
  model="gpt-4o-mini",
  prompt="Generate a summary",
  slo_ms=500
)

Common Pitfalls

Avoid these mistakes that degrade TTFT:

1. Measuring only non-streaming requests: TTFT is invisible in non-streaming calls. Always use streaming for accurate measurement.
2. Ignoring prompt length: A 10,000-token prompt can add 500ms+ to TTFT regardless of model choice.
3. Not testing under load: TTFT under concurrency increases due to queuing. Test with realistic concurrent request rates.
4. Averaging without percentiles: A 300ms average can hide 2-second p99 spikes. Always monitor p95/p99.
5. Overlooking content filtering: Azure OpenAI’s filters add latency. Factor this into your SLOs.
6. Setting max_tokens too high: This reserves unnecessary capacity, potentially increasing TTFT for other requests on shared infrastructure.
7. Using TTFT alone: TTFT tells you when the first token arrives, but not the generation speed. Also monitor Inter-Token Latency (ITL) or Tokens Per Second (TPS).

Quick Reference

Metric	Target (Interactive)	Measurement Method
TTFT	less than 500ms p95	Streaming API, time from request to first token
ITL	less than 50ms avg	Time between consecutive tokens
Goodput	greater than 95%	percent requests meeting TTFT SLO
Prompt Tokens	less than 2000	Keep prompts concise
Max Tokens	Minimal	Set only what’s needed

Cost-Performance Tradeoffs (per 1M tokens):

• GPT-4o mini: $0.15/$0.60 - Best for cost-sensitive apps OpenAI Pricing
• GPT-4o: $5.00/$15.00 - Higher quality, higher latency OpenAI Pricing
• Claude 3.5 Haiku: $0.80/$4.00 - Fast, cost-effective alternative Anthropic Pricing
• Claude 3.5 Sonnet: $3.00/$15.00 - Balanced performance Anthropic Pricing
• Gemini 2.0 Flash: $0.15/$0.60 - Competitive with GPT-4o mini Google Pricing

Widget

Latency breakdown heatmap (by model, batch size, hardware)

Interactive widget derived from “Time to First Token (TTFT) Explained: The Most Important LLM Metric” that lets readers explore latency breakdown heatmap (by model, batch size, hardware).

Key models to cover:

• Anthropic claude-3-5-sonnet (tier: general) — refreshed 2024-11-15
• OpenAI gpt-4o-mini (tier: balanced) — refreshed 2024-10-10
• Anthropic haiku-3.5 (tier: throughput) — refreshed 2024-11-15

Widget metrics to capture: user_selections, calculated_monthly_cost, comparison_delta.

Data sources: model-catalog.json, retrieved-pricing.

Common Pitfalls

Avoid these mistakes that degrade TTFT:

1. Measuring only non-streaming requests: TTFT is invisible in non-streaming calls. Always use streaming for accurate measurement.
2. Ignoring prompt length: A 10,000-token prompt can add 500ms+ to TTFT regardless of model choice.
3. Not testing under load: TTFT under concurrency increases due to queuing. Test with realistic concurrent request rates.
4. Averaging without percentiles: A 300ms average can hide 2-second p99 spikes. Always monitor p95/p99.
5. Overlooking content filtering: Azure OpenAI’s filters add latency. Factor this into your SLOs.
6. Setting max_tokens too high: This reserves unnecessary capacity, potentially increasing TTFT for other requests on shared infrastructure.
7. Using TTFT alone: TTFT tells you when the first token arrives, but not the generation speed. Also monitor Inter-Token Latency (ITL) or Tokens Per Second (TPS).

Quick Reference

Metric	Target (Interactive)	Measurement Method
TTFT	less than 500ms p95	Streaming API, time from request to first token
ITL	less than 50ms avg	Time between consecutive tokens
Goodput	greater than 95%	percent requests meeting TTFT SLO
Prompt Tokens	less than 2000	Keep prompts concise
Max Tokens	Minimal	Set only what’s needed

Cost-Performance Tradeoffs (per 1M tokens):

• GPT-4o mini: $0.15/$0.60 - Best for cost-sensitive apps OpenAI Pricing
• GPT-4o: $5.00/$15.00 - Higher quality, higher latency OpenAI Pricing
• Claude 3.5 Haiku: $0.80/$4.00 - Fast, cost-effective alternative Anthropic Pricing
• Claude 3.5 Sonnet: $3.00/$15.00 - Balanced performance Anthropic Pricing
• Gemini 2.0 Flash: $0.15/$0.60 - Competitive with GPT-4o mini Google Pricing

Summary

Time to First Token is the most critical metric for LLM user experience because it directly controls when users perceive your application as responsive. Unlike total latency, TTFT captures the moment your application stops making the user wait and starts proving it’s working.

Key takeaways:

• TTFT is a composite metric: Network latency + queuing time + prefill phase. Prompt length and system load are often the dominant factors.
• Streaming is non-negotiable: Without streaming, TTFT is invisible. Always use streaming for measurement and production deployment.
• Optimization levers: Keep prompts concise, choose appropriate models, separate workloads, provision dedicated capacity (PTUs), and enable streaming.
• Monitor the right percentiles: Average TTFT is misleading. Track p95/p99 and goodput against SLOs.
• Cost-performance balance: Faster models reduce TTFT but increase cost. Use GPT-4o mini or Claude 3.5 Haiku for most interactive applications.

The difference between 300ms and 800ms TTFT might seem small, but it’s the difference between “instant” and “laggy” in user perception. By measuring accurately, understanding the components, and applying the right optimizations, you can deliver responsive AI experiences that keep users engaged.

Related Resources

Documentation & Guides:

• Azure OpenAI Service performance & latency - Official guide covering TTFT factors, throughput estimation, and optimization strategies
• OpenAI Pricing - Current pricing for GPT-4o, GPT-4o mini, and other models
• Anthropic Pricing - Claude 3.5 Haiku and Sonnet pricing
• Google Vertex AI Pricing - Gemini 2.0 Flash and Pro pricing

Implementation Examples:

• The LLM Latency Guidebook GitHub Repository - Practical notebooks for TTFT optimization techniques including token compression, semantic caching, and parallelization
• TTFT Measurement Notebook - Example code for measuring and optimizing TTFT

Monitoring & Observability:

• Prometheus Client for Python - Build custom TTFT dashboards and alerts
• OpenTelemetry for LLMs - Distributed tracing for request lifecycle analysis

Advanced Topics:

• vLLM Documentation - Explore chunked prefill and other inference optimizations
• Speculative Decoding - Research on reducing TTFT through draft models

For production deployments, start with measuring your current TTFT distribution, then apply optimizations based on your specific bottlenecks (prompt length, model choice, or infrastructure).