Google Gemini API telemetry

Test the integration

Send and verify events with Google Gemini API telemetry

Use Google Gemini API telemetry where your app knows the final result. Collect only the fields you need, then verify a test event before building charts.

1
Choose the outcome
Gemini token and latency analysis
2
Define the contract
provider, model, feature, status, and finish_reason
3
Log the final result
Read token counts from provider response metadata and store the model identifier used for the request.
4
Check the stored event
Exercise a known fixture, then inspect llm_request_completed for one correctly typed terminal row.

Before you start

A server-side GEMINI_API_KEY and TELEMETRY_API_KEY
A stable feature name and versioned cost calculation outside the prompt
A product outcome such as accepted, saved, or completed when the workflow exposes one

Delivery setup

Install and initialize server-side

Import telemetry-sh in server-only code and initialize it once with process.env.TELEMETRY_API_KEY. Keep ingestion credentials out of browser bundles, client-visible environment variables, source control, logs, and exception messages.

google-gemini-install

npm installation

bash

npm install telemetry-sh

1Create one reusable server-side client. Set its timeout and retry limit.
2Log an event when the operation succeeds, fails, retries, or times out.
3Send test events with known results and inspect the stored rows before enabling an alert.

Snippet

Start with one structured event

Add this shape where the workflow completes, fails, or retries. Then build the dashboard from real fields.

google-gemini

Google Gemini API telemetry event

javascript

import { GoogleGenAI } from "@google/genai";
import telemetry from "telemetry-sh";

const gemini = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });
const startedAt = performance.now();

try {
  const response = await gemini.models.generateContent({
    model: process.env.GEMINI_MODEL,
    contents: userInput,
  });
  const usage = response.usageMetadata;

  await telemetry.log("llm_request_completed", {
    provider: "google",
    model: process.env.GEMINI_MODEL,
    feature: "support_draft",
    status: "success",
    latency_ms: Math.round(performance.now() - startedAt),
    input_tokens: usage?.promptTokenCount,
    output_tokens: usage?.candidatesTokenCount,
    total_tokens: usage?.totalTokenCount,
    prompt_version: "support-v4",
  });

  return response;
} catch (error) {
  await telemetry.log("llm_request_completed", {
    provider: "google",
    model: process.env.GEMINI_MODEL,
    feature: "support_draft",
    status: "failed",
    latency_ms: Math.round(performance.now() - startedAt),
    error_type: classifyProviderError(error),
  });
  throw error;
}

Event schema

provider, model, feature, status, and finish_reason

input_tokens, output_tokens, total_tokens, latency_ms, and estimated_cost_usd

request_id, prompt_version, error_type, and reviewed outcome

Check your setup

Checkpoint 1

Read token counts from provider response metadata and store the model identifier used for the request.

Checkpoint 2

Calculate cost from a versioned price table because provider pricing and token classes can change.

Checkpoint 3

Do not send prompt text, uploaded content, generated output, API keys, or provider error bodies to general telemetry.

Verification

Prove the event arrived

Run this after exercising known success and failure cases. Replace the fallback table name if your final event contract differs from the snippet.

google-gemini-verification

Google Gemini API telemetry verification query

sql

SELECT *
FROM llm_request_completed
ORDER BY timestamp_utc DESC
LIMIT 20;

Confirm one terminal row per logical outcome, with the expected status, identifiers, units, and UTC time.

Inspect the inferred schema and verify that retries do not change field types or generate a new logical event ID.

Search the stored fields for credentials, raw payloads, prompts, private content, and unbounded error messages.

Exercise a provider timeout, ingestion rejection, and process shutdown before treating the dashboard as complete.

Implementation references

Review the event contract, data-safety guidance, and upstream primary documentation before enabling a new production path.

OpenAI and LLM cost guide LLM cost SQL AI quality SQL Gemini API text generation

Where to log

Keep the outcome event small and recoverable

This pattern provides

Record the outcome as an event you can query with SQL.
Stable fields for dashboards, alerts, and cross-event correlation.
Test events for checking success, failure, retries, and timeouts.

This pattern does not provide

An OTLP exporter, automatic collection pipeline, or replacement for detailed traces and diagnostic logs.
Exactly-once delivery merely because the payload contains an event ID.
Permission to collect raw provider payloads, user content, credentials, or regulated data.

Delivery and idempotency Batching and shutdown Troubleshoot ingestion

Example event schemas

Check what each event records, when to send it, and which field types it needs. Review the example payload and privacy checklist before using it in production.

AI operations10 fields