{"slug": "ollama-monitoring-observability-with-opentelemetry", "title": "Ollama Monitoring & Observability with OpenTelemetry", "summary": "SigNoz now supports full observability for Ollama LLM workloads through OpenTelemetry, enabling developers to collect traces, logs, and metrics from local Python applications. The integration allows teams to correlate telemetry data in unified dashboards, configure alerts for latency and errors, and improve reliability of LLM deployments using automatic or manual instrumentation.", "body_md": "What is Ollama Monitoring?\n\nOllama monitoring with OpenTelemetry lets you collect traces, logs, and metrics from your local LLM workloads and export them to SigNoz. This guide walks you through setting up full Ollama observability using [OpenTelemetry](https://opentelemetry.io/), covering automatic and manual instrumentation for Python applications.\n\nWith full Ollama monitoring in SigNoz, you can correlate traces, logs, and metrics in unified dashboards, configure alerts for latency and errors, and gain actionable insights to improve the reliability and responsiveness of your LLM workloads.\n\nPrerequisites\n\n- A\n[SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key or[Self Hosted SigNoz instance](https://signoz.io/docs/install/self-host/) - Internet access to send telemetry data to SigNoz Cloud\n- Python 3.10+ with\n`ollama`\n\ninstalled - For Python:\n`pip`\n\ninstalled for managing Python packages\n\nOllama Monitoring with OpenTelemetry\n\nFor more information on getting started with Ollama in your Python environment, refer to the [Ollama quickstart guide](https://docs.ollama.com/quickstart#python).\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\nStep 1: Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-distro \\\n  opentelemetry-exporter-otlp \\\n  httpx \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  ollama \\\n  opentelemetry-instrumentation-ollama\n```\n\nStep 2: Add Automatic Instrumentation\n\n```\nopentelemetry-bootstrap --action=install\n```\n\nStep 3: Configure logging level\n\nTo ensure logs are properly captured and exported, configure the root logger to emit logs at the DEBUG level or higher:\n\n``` python\nimport logging\n\nlogging.getLogger().setLevel(logging.DEBUG)\nlogging.getLogger(\"httpx\").setLevel(logging.DEBUG)\n```\n\nThis sets the minimum log level for the root logger to DEBUG, which ensures that `logger.debug()`\n\ncalls and higher severity logs (INFO, WARNING, ERROR, CRITICAL) are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz.\n\nStep 4: Create an example Ollama application\n\nStart by downloading a model:\n\n```\nollama pull gemma3\n```\n\nCreate your Python app:\n\n``` python\nfrom ollama import chat\nfrom ollama import ChatResponse\nimport logging\n\nlogging.getLogger().setLevel(logging.DEBUG)\nlogging.getLogger(\"httpx\").setLevel(logging.DEBUG)\n\nresponse: ChatResponse = chat(model='gemma3', messages=[\n  {\n    'role': 'user',\n    'content': 'What is SigNoz?',\n  },\n])\nprint(response['message']['content'])\n```\n\nStep 5: Run your application with auto-instrumentation\n\nRun your application with the following environment variables set. This configures OpenTelemetry to export traces, logs, and metrics to SigNoz Cloud and enables automatic log correlation:\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. In this case we would use:`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 manual 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\nStep 1: Install additional OpenTelemetry dependencies\n\n```\npip install \\\n  opentelemetry-api \\\n  opentelemetry-sdk \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  ollama \\\n  opentelemetry-instrumentation-ollama\n```\n\nStep 2: Import the necessary modules in your Python application\n\n**Traces:**\n\n``` python\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\nfrom opentelemetry.instrumentation.ollama import OllamaInstrumentor\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\nStep 3: Set up Traces\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\nfrom opentelemetry.instrumentation.ollama import OllamaInstrumentor().instrument()\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\nOllamaInstrumentor().instrument(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\nStep 4: Set up 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\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 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\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\nSystemMetricsInstrumentor provides system metrics (CPU, memory, etc.), and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. If you want to add custom metrics to your Ollama application, see [Python Custom Metrics](https://signoz.io/opentelemetry/python-custom-metrics/).\n\nStep 5: Set up Logs\n\n``` python\nimport logging\nfrom opentelemetry.sdk.resources import Resource\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\nimport os\n\nresource = Resource.create({\"service.name\": \"<service_name>\"})\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# Enable httpx logging to capture HTTP requests\nhttpx_logger = logging.getLogger(\"httpx\")\nhttpx_logger.setLevel(logging.DEBUG)\nhttpx_logger.addHandler(handler)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud 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\nStep 6: Run an example Ollama application\n\nEnsure you have completed the steps above (traces, logs, and metrics configuration) before running this code. All OpenTelemetry instrumentation must be initialized first.\n\nStart by downloading a model:\n\n```\nollama pull gemma3\n```\n\nCreate your Python app:\n\n``` python\nfrom ollama import chat\nfrom ollama import ChatResponse\n\nresponse: ChatResponse = chat(model='gemma3', messages=[\n  {\n    'role': 'user',\n    'content': 'What is SigNoz?',\n  },\n])\nprint(response['message']['content'])\n```\n\nView Ollama Traces, Logs, and Metrics in SigNoz\n\nOnce configured, your Ollama application automatically emits traces, logs, and metrics.\n\nOllama traces are available in SigNoz 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\nOllama logs are available in SigNoz under the Logs tab. Click the “Related Logs” button in the trace view to see correlated logs:\n\nWhen you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:\n\nOllama metrics are available in SigNoz 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\nTroubleshooting Ollama Monitoring\n\n[Troubleshooting Ollama Monitoring](#troubleshooting-ollama-monitoring)\n\nIf you don't see your telemetry data:\n\n**Verify network connectivity**- Ensure your application can reach SigNoz Cloud endpoints** Check ingestion key**- Verify your SigNoz ingestion key is correct** Wait for data**- OpenTelemetry batches data before sending, so wait 10-30 seconds after making API calls** Try a console exporter**— Enable a console exporter locally to confirm that your application is generating telemetry data before it’s sent to SigNoz\n\nOllama Monitoring Dashboard\n\nYou can also check out the [Ollama Dashboard template](https://signoz.io/docs/dashboards/dashboard-templates/ollama-dashboard/) which provides specialized visualizations for monitoring your Ollama usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.\n\nSetup OpenTelemetry Collector (Optional)\n\n[Setup OpenTelemetry Collector (Optional)](#setup-opentelemetry-collector-optional)\n\nWhat is the OpenTelemetry Collector?\n\nThink of the OTel Collector as a middleman between your app and SigNoz. Instead of your application sending data directly to SigNoz, it sends everything to the Collector first, which then forwards it along.\n\nWhy use it?\n\n**Cleaning up data**— Filter out noisy traces you don't care about, or remove sensitive info before it leaves your servers.** Keeping your app lightweight**— Let the Collector handle batching, retries, and compression instead of your application code.** Adding context automatically**— The Collector can tag your data with useful info like which Kubernetes pod or cloud region it came from.** Future flexibility**— Want to send data to multiple backends later? The Collector makes that easy without changing your app.\n\nSee [Switch from direct export to Collector](https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/switch-to-collector/) for step-by-step instructions to convert your setup.\n\nFor more details, see [Why use the OpenTelemetry Collector?](https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/why-to-use-collector/) and the [Collector configuration guide](https://signoz.io/docs/opentelemetry-collection-agents/opentelemetry-collector/configuration/).\n\nAdditional resources:\n\n- Set up\n[alerts](https://signoz.io/docs/product-features/alert-management)for high latency or error rates - Learn more about\n[querying traces](https://signoz.io/docs/product-features/trace-explorer) - Explore\n[log correlation](https://signoz.io/docs/product-features/logs-explorer)", "url": "https://wpnews.pro/news/ollama-monitoring-observability-with-opentelemetry", "canonical_source": "https://signoz.io/docs/ollama-monitoring", "published_at": "2026-05-27 00:00:00+00:00", "updated_at": "2026-05-29 16:26:04.055077+00:00", "lang": "en", "topics": ["large-language-models", "mlops", "ai-tools", "ai-infrastructure", "artificial-intelligence"], "entities": ["Ollama", "OpenTelemetry", "SigNoz", "Python"], "alternates": {"html": "https://wpnews.pro/news/ollama-monitoring-observability-with-opentelemetry", "markdown": "https://wpnews.pro/news/ollama-monitoring-observability-with-opentelemetry.md", "text": "https://wpnews.pro/news/ollama-monitoring-observability-with-opentelemetry.txt", "jsonld": "https://wpnews.pro/news/ollama-monitoring-observability-with-opentelemetry.jsonld"}}