Why TTFT Matters

When a user submits a prompt, the Time to First Token (TTFT) is the moment the first token of the model’s response appears. For chat‑based applications, TTFT directly translates to perceived latency; a delay of even 200 ms can feel sluggish. Traditional monitoring tools focus on overall response time, but TTFT isolates the initiation phase, revealing bottlenecks in model warm‑up, network latency, or provider scheduling.

What the Metrik API Offers

Metrik’s endpoint aggregates TTFT data for more than 26 models spanning four major vendors:

  • OpenAI (gpt‑4, gpt‑3.5‑turbo, etc.)
  • Anthropic (Claude‑2, Claude‑3)
  • Google (Gemini, PaLM)
  • xAI (Llama‑2, etc.)

The API refreshes every hour, returning a JSON payload that includes:

Field Description
model Model identifier
provider Vendor name
ttft_ms Current TTFT in milliseconds
provider_avg_ms Average TTFT across all models from the same provider
change_pct Percentage change since the previous hour 
GET https://metrik-dashboard.vercel.app/api/ttft
Accept: application/json

Response

{
  "data": [
    {
      "model": "gpt-4o",
      "provider": "OpenAI",
      "ttft_ms": 320,
      "provider_avg_ms": 280,
      "change_pct": -5.4
    },
    {
      "model": "claude-3-5-sonnet",
      "provider": "Anthropic",
      "ttft_ms": 410,
      "provider_avg_ms": 395,
      "change_pct": +2.1
    }
  ],
  "last_updated": "2025-12-15T02:00:00Z"
}

Rate Limiting and Scaling

Each response includes HTTP headers that expose the current rate‑limit window:

  • X-RateLimit-Limit – Total requests allowed per hour
  • X-RateLimit-Remaining – Requests left in the current window
  • X-RateLimit-Reset – Unix timestamp when the window resets

These headers enable developers to programmatically back‑off or queue requests, ensuring compliance with the service’s limits. For high‑throughput use cases, Metrik offers custom rate limits upon request.

How It Helps Developers

  • Model Selection – By comparing TTFT across providers, teams can choose the model that delivers the fastest start time for a given use case.
  • Performance Regression Detection – The change_pct field flags sudden latency spikes, allowing rapid investigation before users notice.
  • Cost‑Latency Trade‑Offs – Combining TTFT data with pricing APIs lets engineers balance response speed against token cost.

The Bigger Picture

Latency in LLM services is a moving target. Providers continuously tweak infrastructure, and network conditions fluctuate. A real‑time telemetry layer like Metrik’s TTFT API turns opaque performance into actionable data, enabling teams to iterate on model choice, prompt design, and deployment topology with confidence.

By exposing granular latency metrics and provider‑wide averages, Metrik empowers developers to keep the user experience snappy while navigating the rapidly evolving LLM ecosystem.