# Ollama Monitoring & Observability with OpenTelemetry

> Source: <https://signoz.io/docs/ollama-monitoring>
> Published: 2026-05-27 00:00:00+00:00

What is Ollama Monitoring?

Ollama 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.

With 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.

Prerequisites

- A
[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
- Python 3.10+ with
`ollama`

installed - For Python:
`pip`

installed for managing Python packages

Ollama Monitoring with OpenTelemetry

For more information on getting started with Ollama in your Python environment, refer to the [Ollama quickstart guide](https://docs.ollama.com/quickstart#python).

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 \
  httpx \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  ollama \
  opentelemetry-instrumentation-ollama
```

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 DEBUG level or higher:

``` python
import logging

logging.getLogger().setLevel(logging.DEBUG)
logging.getLogger("httpx").setLevel(logging.DEBUG)
```

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

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

Step 4: Create an example Ollama application

Start by downloading a model:

```
ollama pull gemma3
```

Create your Python app:

``` python
from ollama import chat
from ollama import ChatResponse
import logging

logging.getLogger().setLevel(logging.DEBUG)
logging.getLogger("httpx").setLevel(logging.DEBUG)

response: ChatResponse = chat(model='gemma3', messages=[
  {
    'role': 'user',
    'content': 'What is SigNoz?',
  },
])
print(response['message']['content'])
```

Step 5: Run your application with auto-instrumentation

Run 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:

```
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. In this case we would use:`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 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.

Step 1: Install additional OpenTelemetry dependencies

```
pip install \
  opentelemetry-api \
  opentelemetry-sdk \
  opentelemetry-exporter-otlp \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  ollama \
  opentelemetry-instrumentation-ollama
```

Step 2: Import the necessary modules in your Python application

**Traces:**

``` python
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
from opentelemetry.instrumentation.ollama import OllamaInstrumentor
```

**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 Traces

``` 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
from opentelemetry.instrumentation.ollama import OllamaInstrumentor().instrument()
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)

OllamaInstrumentor().instrument(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: Set up 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
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 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/)

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).

SystemMetricsInstrumentor 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/).

Step 5: Set up Logs

``` python
import logging
from opentelemetry.sdk.resources import Resource
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
import os

resource = Resource.create({"service.name": "<service_name>"})
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__)

# Enable httpx logging to capture HTTP requests
httpx_logger = logging.getLogger("httpx")
httpx_logger.setLevel(logging.DEBUG)
httpx_logger.addHandler(handler)
```

is the name of your service`<service_name>`

→ SigNoz Cloud 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: Run an example Ollama application

Ensure you have completed the steps above (traces, logs, and metrics configuration) before running this code. All OpenTelemetry instrumentation must be initialized first.

Start by downloading a model:

```
ollama pull gemma3
```

Create your Python app:

``` python
from ollama import chat
from ollama import ChatResponse

response: ChatResponse = chat(model='gemma3', messages=[
  {
    'role': 'user',
    'content': 'What is SigNoz?',
  },
])
print(response['message']['content'])
```

View Ollama Traces, Logs, and Metrics in SigNoz

Once configured, your Ollama application automatically emits traces, logs, and metrics.

Ollama traces are available in SigNoz 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.

Ollama logs are available in SigNoz under the Logs tab. Click the “Related Logs” button in the trace view to see correlated logs:

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

Ollama metrics are available in SigNoz 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:

Troubleshooting Ollama Monitoring

[Troubleshooting Ollama Monitoring](#troubleshooting-ollama-monitoring)

If you don't see your telemetry data:

**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

Ollama Monitoring Dashboard

You 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.

Setup OpenTelemetry Collector (Optional)

[Setup OpenTelemetry Collector (Optional)](#setup-opentelemetry-collector-optional)

What is the OpenTelemetry Collector?

Think 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.

Why use it?

**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.

See [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.

For 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/).

Additional resources:

- Set up
[alerts](https://signoz.io/docs/product-features/alert-management)for high latency or error rates - Learn more about
[querying traces](https://signoz.io/docs/product-features/trace-explorer) - Explore
[log correlation](https://signoz.io/docs/product-features/logs-explorer)