LLM Cost Tracking for SaaS: The Complete Setup Guide

LLM cost tracking setup guide: Start With a Checklist

Instrumentation works best when every LLM request carries a meaningful feature name. With one async call after your model response, the dashboard starts showing cost by feature, model, user, latency, and status.

LLM cost tracking setup guide for Feature Taxonomy

LLMtrack records model, feature name, token counts, latency, status, and computed cost after every LLM response. That turns optimization from a guessing exercise into a ranked list of actions based on your own production traffic.

Copy// Fire-and-forget: never blocks users
fetch('https://llm-track.com/api/ingest', {
  method: 'POST',
  headers: { 'x-api-key': process.env.LLMTRACK_KEY },
  body: JSON.stringify({
    provider: 'openai',
    model: response.model,
    feature_name: 'chat-completion',
    total_tokens: response.usage.total_tokens,
    latency_ms: Date.now() - startedAt,
    status: 'success'
  })
}).catch(() => {})

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