{"slug": "google-gemini-monitoring-observability-with-opentelemetry", "title": "Google Gemini Monitoring & Observability with OpenTelemetry", "summary": "SigNoz released a technical guide for monitoring Google Gemini API performance using OpenTelemetry, enabling developers to export logs, traces, and metrics to the SigNoz observability platform. The integration provides real-time visibility into latency, error rates, and usage trends for Gemini-based applications through unified dashboards and alerts. The guide covers both no-code auto-instrumentation and code-based setup, requiring a SigNoz account and a Google Gemini API key.", "body_md": "Overview\n\nThis 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.\n\nInstrumenting 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.\n\nPrerequisites\n\n- SigNoz setup (choose one):\n[SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key- Self-hosted SigNoz instance\n\n- Internet access to send telemetry data to SigNoz Cloud\n- A\n[Google Gemini](https://ai.google.dev/gemini-api/docs/libraries)API account with a working API Key `pip`\n\ninstalled for managing Python packages*(Optional but recommended)*A Python virtual environment to isolate dependencies\n\nMonitoring Google Gemini\n\nNo-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.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-distro \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  google-genai \\\n  openinference-instrumentation-google-genai\n```\n\n**Step 2:** Add Automatic Instrumentation\n\n```\nopentelemetry-bootstrap --action=install\n```\n\n**Step 3:** Configure logging level\n\nTo ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher:\n\n``` python\nimport logging\n\nlogging.getLogger().setLevel(logging.INFO)\n```\n\nThis sets the minimum log level for the root logger to INFO, which ensures that `logger.info()`\n\ncalls and higher severity logs (WARNING, ERROR, CRITICAL) are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz.\n\n**Step 4:** Run an example\n\n``` python\nfrom google import genai\n\nclient = genai.Client()\n\nresponse = client.models.generate_content(\n    model=\"gemini-2.5-flash\",\n    contents=\"What is SigNoz?\",\n)\nprint(response.text)\n```\n\n📌 Note: Ensure that the\n\n`GEMINI_API_KEY`\n\nenvironment variable is properly defined with your API key before running the code.\n\n**Step 5:** Run your application with auto-instrumentation\n\n```\nOTEL_RESOURCE_ATTRIBUTES=\"service.name=<service_name>\" \\\nOTEL_EXPORTER_OTLP_ENDPOINT=\"https://ingest.<region>.signoz.cloud:443\" \\\nOTEL_EXPORTER_OTLP_HEADERS=\"signoz-ingestion-key=<your-ingestion-key>\" \\\nOTEL_EXPORTER_OTLP_PROTOCOL=grpc \\\nOTEL_TRACES_EXPORTER=otlp \\\nOTEL_METRICS_EXPORTER=otlp \\\nOTEL_LOGS_EXPORTER=otlp \\\nOTEL_PYTHON_LOG_CORRELATION=true \\\nOTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \\\nopentelemetry-instrument <your_run_command>\n```\n\nis the name of your service`<service_name>`\n\n`<region>`\n\n: Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`<your-ingestion-key>`\n\n: Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace\n`<your_run_command>`\n\nwith the actual command you would use to run your application. For example:`python main.py`\n\nUsing 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).\n\nCode-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.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-api \\\n  opentelemetry-sdk \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-system-metrics \\\n  opentelemetry-instrumentation-httpx \\\n  google-genai \\\n  openinference-instrumentation-google-genai\n```\n\n**Step 2:** Import the necessary modules in your Python application\n\n**Traces:**\n\n``` python\nfrom openinference.instrumentation.google_genai import GoogleGenAIInstrumentor\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n```\n\n**Logs:**\n\n``` python\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nfrom opentelemetry._logs import set_logger_provider\nimport logging\n```\n\n**Metrics:**\n\n``` python\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nfrom opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor\n```\n\n**Step 3:** Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\nfrom opentelemetry import trace\nimport os\n\nresource = Resource.create({\"service.name\": \"<service_name>\"})\nprovider = TracerProvider(resource=resource)\nspan_exporter = OTLPSpanExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_TRACES_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nprocessor = BatchSpanProcessor(span_exporter)\nprovider.add_span_processor(processor)\ntrace.set_tracer_provider(provider)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/traces`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing 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).\n\n**Step 4:** Instrument Google Gemini using `GoogleGenAIInstrumentor`\n\nand the configured Tracer Provider\n\n``` python\nfrom openinference.instrumentation.google_genai import GoogleGenAIInstrumentor\n\nGoogleGenAIInstrumentor().instrument(tracer_provider=provider)\n```\n\n📌 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.\n\n**Step 5**: Setup Logs\n\n``` python\nimport logging\nimport os\nfrom opentelemetry._logs import set_logger_provider\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\n\nlogger_provider = LoggerProvider(resource=resource)\nset_logger_provider(logger_provider)\n\notlp_log_exporter = OTLPLogExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_LOGS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nlogger_provider.add_log_record_processor(\n    BatchLogRecordProcessor(otlp_log_exporter)\n)\n# Attach OTel logging handler to root logger\nhandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)\nlogging.basicConfig(level=logging.INFO, handlers=[handler])\n\nlogger = logging.getLogger(__name__)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud logs endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/logs`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing 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).\n\n**Step 6**: Setup Metrics\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nfrom opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor\nimport os\n\nresource = Resource.create({\"service.name\": \"<service-name>\"})\nmetric_exporter = OTLPMetricExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_METRICS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nreader = PeriodicExportingMetricReader(metric_exporter)\nmetric_provider = MeterProvider(metric_readers=[reader], resource=resource)\nmetrics.set_meter_provider(metric_provider)\n\nmeter = metrics.get_meter(__name__)\n\n# turn on out-of-the-box metrics\nSystemMetricsInstrumentor().instrument()\nHTTPXClientInstrumentor().instrument()\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud metrics endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/metrics`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\n📌 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\n\n[Python Custom Metrics].\n\nUsing 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).\n\n**Step 7:** Run an example\n\n``` python\nfrom google import genai\n\nclient = genai.Client()\n\nresponse = client.models.generate_content(\n    model=\"gemini-2.5-flash\",\n    contents=\"What is SigNoz?\",\n)\nprint(response.text)\n```\n\n📌 Note: Ensure that the\n\n`GEMINI_API_KEY`\n\nenvironment variable is properly defined with your API key before running the code.\n\nView Traces, Logs, and Metrics in SigNoz\n\nYour Google Gemini commands should now automatically emit traces, logs, and metrics.\n\nYou should be able to view traces in Signoz Cloud under the traces tab:\n\nWhen 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.\n\nYou can view logs by clicking on the “Related Logs” button in the trace view to see correlated logs for a given trace:\n\nYou should also be able to view logs in Signoz Cloud under the logs tab:\n\nWhen you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:\n\nYou should be able to see Gemini metrics in Signoz Cloud under the metrics tab:\n\nWhen you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:\n\nDashboard\n\nYou 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.", "url": "https://wpnews.pro/news/google-gemini-monitoring-observability-with-opentelemetry", "canonical_source": "https://signoz.io/docs/google-gemini-monitoring", "published_at": "2026-06-11 00:00:00+00:00", "updated_at": "2026-06-12 10:00:05.868085+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "generative-ai", "ai-tools"], "entities": ["Google Gemini", "OpenTelemetry", "SigNoz", "Google"], "alternates": {"html": "https://wpnews.pro/news/google-gemini-monitoring-observability-with-opentelemetry", "markdown": "https://wpnews.pro/news/google-gemini-monitoring-observability-with-opentelemetry.md", "text": "https://wpnews.pro/news/google-gemini-monitoring-observability-with-opentelemetry.txt", "jsonld": "https://wpnews.pro/news/google-gemini-monitoring-observability-with-opentelemetry.jsonld"}}