Track OpenAI Job TokenstokensThe pieces of text an AI model processes. Token counts are often used to measure usage and calculate model cost.View glossary entrySource: OpenAI token guide, CostcostThe money associated with a run, often estimated from token usage and provider pricing. TelemHQ can store cost fields when your job sends them.View glossary entrySource: OpenAI token guide, And LatencylatencyHow long a request or job takes to respond. AI job latency helps teams spot slow model calls, overloaded workers, or expensive retries.View glossary entrySource: MDN glossary With One POSTPOSTAn HTTP method used to send data to a server. TelemHQ pings use POST when a job reports a run and optional payload.View glossary entrySource: MDN HTTP docs

1. Create A TrackertrackerA monitored job, AI pipeline, worker, script, or automation in TelemHQ. Each tracker has its own ping URL and run history.View glossary entrySource: TelemHQ docs

Create a TelemHQ tracker for the OpenAI job. Add a croncronA Unix-style scheduler for recurring commands. TelemHQ uses cron schedules to know when a tracker should receive its next ping.View glossary entrySource: AWS EventBridge Scheduler docs schedule if the job should run on time, or leave the schedule blank for queue workersworkerA background process that performs work outside the main request path, such as syncing data, generating reports, or running AI tasks.View glossary entrySource: GitHub Actions CI docs, agentsagentAn AI application that uses a model, instructions, state, and tools to work toward a goal. Agents are useful to monitor because they can run for a while and make multiple tool calls.View glossary entrySource: Google Cloud Generative AI glossary, and manually triggered jobs.

Copy the tracker ping URL. It will look like https://telemhq.com/ping/YOUR_TOKEN.

2. Send Operational MetadatametadataData about a run rather than the private content of the run itself, such as model name, duration, branch, item counts, or token totals.View glossary entrySource: MDN API glossary

Keep prompts, completions, retrieved documents, and customer content out of the payloadpayloadThe structured data sent with a request. In TelemHQ, payloads should contain safe operational metadata, not prompts, completions, secrets, customer data, or private paths.View glossary entrySource: MDN API glossary unless you intentionally want them stored. For most jobs, operational metadata is enough.

{
  "provider": "openai",
  "model": "gpt-5",
  "project": "support-digest",
  "status": "success",
  "input_tokens": 8120,
  "output_tokens": 940,
  "total_tokens": 9060,
  "latency_ms": 1840,
  "cost_usd": 0.42,
  "items_processed": 128,
  "items_failed": 0,
  "eval_score": 0.91
}

3. Add The Ping In Node.js

import OpenAI from "openai";

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const model = process.env.OPENAI_MODEL || "gpt-5";
const startedAt = Date.now();

try {
  const response = await client.responses.create({
    model,
    input: "Summarize today's support tickets."
  });

  const usage = response.usage || {};

  await fetch(process.env.TELEMHQ_PING_URL, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      provider: "openai",
      model: response.model || model,
      project: "support-digest",
      status: "success",
      input_tokens: usage.input_tokens || 0,
      output_tokens: usage.output_tokens || 0,
      total_tokens: usage.total_tokens || 0,
      latency_ms: Date.now() - startedAt,
      items_processed: 1,
      items_failed: 0
    })
  });
} catch (error) {
  await fetch(process.env.TELEMHQ_PING_URL, {
    method: "POST",
    headers: { "Content-Type": "application/json" },
    body: JSON.stringify({
      provider: "openai",
      model,
      project: "support-digest",
      status: "error",
      latency_ms: Date.now() - startedAt,
      items_processed: 0,
      items_failed: 1,
      error: error instanceof Error ? error.message : "Unknown error"
    })
  });

  throw error;
}