# Google Gemini Monitoring & Observability with OpenTelemetry

> Source: <https://signoz.io/docs/google-gemini-monitoring>
> Published: 2026-06-11 00:00:00+00:00

Overview

This guide walks you through setting up monitoring and observability for Google Gemini API using [OpenTelemetry](https://opentelemetry.io/) and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe model performance, capture request/response details, and track system-level metrics in SigNoz, giving you real-time visibility into latency, error rates, and usage trends for your Gemini applications.

Instrumenting Gemini in your LLM applications with telemetry ensures full observability across your AI workflows, making it easier to debug issues, optimize performance, and understand user interactions. By leveraging SigNoz, you can analyze correlated traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to continuously improve reliability, responsiveness, and user experience.

Prerequisites

- SigNoz setup (choose one):
[SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key- Self-hosted SigNoz instance

- Internet access to send telemetry data to SigNoz Cloud
- A
[Google Gemini](https://ai.google.dev/gemini-api/docs/libraries)API account with a working API Key `pip`

installed for managing Python packages*(Optional but recommended)*A Python virtual environment to isolate dependencies

Monitoring Google Gemini

No-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries.

**Step 1:** Install the necessary packages in your Python environment.

```
pip install \
  opentelemetry-distro \
  opentelemetry-exporter-otlp \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  google-genai \
  openinference-instrumentation-google-genai
```

**Step 2:** Add Automatic Instrumentation

```
opentelemetry-bootstrap --action=install
```

**Step 3:** Configure logging level

To ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher:

``` python
import logging

logging.getLogger().setLevel(logging.INFO)
```

This sets the minimum log level for the root logger to INFO, which ensures that `logger.info()`

calls and higher severity logs (WARNING, ERROR, CRITICAL) are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz.

**Step 4:** Run an example

``` python
from google import genai

client = genai.Client()

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What is SigNoz?",
)
print(response.text)
```

📌 Note: Ensure that the

`GEMINI_API_KEY`

environment variable is properly defined with your API key before running the code.

**Step 5:** Run your application with auto-instrumentation

```
OTEL_RESOURCE_ATTRIBUTES="service.name=<service_name>" \
OTEL_EXPORTER_OTLP_ENDPOINT="https://ingest.<region>.signoz.cloud:443" \
OTEL_EXPORTER_OTLP_HEADERS="signoz-ingestion-key=<your-ingestion-key>" \
OTEL_EXPORTER_OTLP_PROTOCOL=grpc \
OTEL_TRACES_EXPORTER=otlp \
OTEL_METRICS_EXPORTER=otlp \
OTEL_LOGS_EXPORTER=otlp \
OTEL_PYTHON_LOG_CORRELATION=true \
OTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \
opentelemetry-instrument <your_run_command>
```

is the name of your service`<service_name>`

`<region>`

: Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`<your-ingestion-key>`

: Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace
`<your_run_command>`

with the actual command you would use to run your application. For example:`python main.py`

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).

Code-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure.

**Step 1:** Install the necessary packages in your Python environment.

```
pip install \
  opentelemetry-api \
  opentelemetry-sdk \
  opentelemetry-exporter-otlp \
  opentelemetry-instrumentation-system-metrics \
  opentelemetry-instrumentation-httpx \
  google-genai \
  openinference-instrumentation-google-genai
```

**Step 2:** Import the necessary modules in your Python application

**Traces:**

``` python
from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor
from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
```

**Logs:**

``` python
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
from opentelemetry._logs import set_logger_provider
import logging
```

**Metrics:**

``` python
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor
```

**Step 3:** Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud

``` python
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry import trace
import os

resource = Resource.create({"service.name": "<service_name>"})
provider = TracerProvider(resource=resource)
span_exporter = OTLPSpanExporter(
    endpoint= os.getenv("OTEL_EXPORTER_TRACES_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
processor = BatchSpanProcessor(span_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
```

is the name of your service`<service_name>`

→ SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT`

[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/traces`

→ Your SigNoz`SIGNOZ_INGESTION_KEY`

[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).

**Step 4:** Instrument Google Gemini using `GoogleGenAIInstrumentor`

and the configured Tracer Provider

``` python
from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor

GoogleGenAIInstrumentor().instrument(tracer_provider=provider)
```

📌 Important: Place this code at the start of your application logic — before any Gemini functions are called or used — to ensure telemetry is correctly captured.

**Step 5**: Setup Logs

``` python
import logging
import os
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter

logger_provider = LoggerProvider(resource=resource)
set_logger_provider(logger_provider)

otlp_log_exporter = OTLPLogExporter(
    endpoint= os.getenv("OTEL_EXPORTER_LOGS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(otlp_log_exporter)
)
# Attach OTel logging handler to root logger
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.basicConfig(level=logging.INFO, handlers=[handler])

logger = logging.getLogger(__name__)
```

is the name of your service`<service_name>`

→ SigNoz Cloud logs endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT`

[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/logs`

→ Your SigNoz`SIGNOZ_INGESTION_KEY`

[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).

**Step 6**: Setup Metrics

``` python
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor
import os

resource = Resource.create({"service.name": "<service-name>"})
metric_exporter = OTLPMetricExporter(
    endpoint= os.getenv("OTEL_EXPORTER_METRICS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
reader = PeriodicExportingMetricReader(metric_exporter)
metric_provider = MeterProvider(metric_readers=[reader], resource=resource)
metrics.set_meter_provider(metric_provider)

meter = metrics.get_meter(__name__)

# turn on out-of-the-box metrics
SystemMetricsInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()
```

is the name of your service`<service_name>`

→ SigNoz Cloud metrics endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT`

[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/metrics`

→ Your SigNoz`SIGNOZ_INGESTION_KEY`

[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)

📌 Note: SystemMetricsInstrumentor provides system metrics (CPU, memory, etc.), and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. These are not Gemini-specific metrics. Gemini does not expose metrics as part of their SDK. If you want to add custom metrics to your Gemini application, see

[Python Custom Metrics].

Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).

**Step 7:** Run an example

``` python
from google import genai

client = genai.Client()

response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="What is SigNoz?",
)
print(response.text)
```

📌 Note: Ensure that the

`GEMINI_API_KEY`

environment variable is properly defined with your API key before running the code.

View Traces, Logs, and Metrics in SigNoz

Your Google Gemini commands should now automatically emit traces, logs, and metrics.

You should be able to view traces in Signoz Cloud under the traces tab:

When you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes.

You can view logs by clicking on the “Related Logs” button in the trace view to see correlated logs for a given trace:

You should also be able to view logs in Signoz Cloud under the logs tab:

When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:

You should be able to see Gemini metrics in Signoz Cloud under the metrics tab:

When you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:

Dashboard

You can also check out our custom Google Gemini dashboard [here](https://signoz.io/docs/dashboards/dashboard-templates/google-gemini-dashboard/) which provides specialized visualizations for monitoring your Gemini API usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.