> For the complete documentation index, see [llms.txt](https://developer.harness.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.harness.io/cloud-cost-management/integrations/sdk-integrations/manual-instrumentation.md).

# Manual Instrumentation

Use this page for Go, Java, .NET, or any language the Harness SDK does not cover. You instrument LLM calls using a standard OpenTelemetry SDK and set the GenAI attributes directly — there is no wrapper. You need an ingestion token before you start; go to the [AI Cost Management Quickstart](/cloud-cost-management/new-to-cacm/ai-cost-management/quickstart.md) to get one.

{% hint style="info" %}
**GENAI SEMANTIC CONVENTIONS REQUIRED**

Cost is calculated from OpenTelemetry traces with GenAI semantic conventions. Go to the [GenAI Span Attribute Reference](/cloud-cost-management/references/ai-cost-management/genai-span-attribute-reference.md) to review the attributes CACM reads.
{% endhint %}

***

### Required Attributes <a href="#required-attributes" id="required-attributes"></a>

At minimum, each LLM span must set:

* `gen_ai.provider.name` (preferred; the legacy `gen_ai.system` is also supported)
* `gen_ai.request.model`
* `gen_ai.usage.input_tokens`
* `gen_ai.usage.output_tokens`
* `gen_ai.agent.name` (so the cost can be associated with an agent)

Export the spans over OTLP to the Harness endpoint.

***

### Instrument an LLM Call <a href="#instrument-an-llm-call" id="instrument-an-llm-call"></a>

Reuse the OpenTelemetry exporter setup from the [Harness SDK](/cloud-cost-management/integrations/sdk-integrations/harness-sdk.md) or any framework page — that registers the global tracer provider that `trace.get_tracer()` reads from. Then wrap each LLM call in a span and set the GenAI attributes:

```python
from opentelemetry import trace

tracer = trace.get_tracer(__name__)

with tracer.start_as_current_span("llm.call") as span:
    span.set_attribute("gen_ai.provider.name", "openai")  # preferred; "gen_ai.system" also supported
    span.set_attribute("gen_ai.agent.name", "support-copilot")  # required for cost attribution
    span.set_attribute("gen_ai.request.model", "gpt-4-turbo")
    # ... make the LLM call ...
    span.set_attribute("gen_ai.usage.input_tokens", resp.usage.prompt_tokens)
    span.set_attribute("gen_ai.usage.output_tokens", resp.usage.completion_tokens)
```

For other languages, use the equivalent OpenTelemetry SDK (Go, Java, .NET) and set the same attributes.

***

### Reduce Trace Data Volume <a href="#reduce-trace-data-volume" id="reduce-trace-data-volume"></a>

Large payloads and over-instrumentation inflate span volume and storage cost. To keep trace data manageable in high-traffic production:

* **Scope instrumentation to LLM calls:** Instrument the model calls that carry cost, not every function in the application.
* **Sample a percentage of traces:** Export a representative sample rather than every trace.

***

### Verify Traces in Cost Explorer <a href="#verify-traces-in-cost-explorer" id="verify-traces-in-cost-explorer"></a>

1. Run the application so traces flow. They usually appear within a few minutes; allow up to about 20 minutes.
2. Go to **Cloud & AI Cost Management** > **Cost Explorer**.
3. Select the **AI Traces** view or group by **Service Name**, and find your service.
4. Select a service row to open the **Service Traces** drawer and inspect the span waterfall.

***

### Next Steps <a href="#next-steps" id="next-steps"></a>

* Go to the [GenAI Span Attribute Reference](/cloud-cost-management/references/ai-cost-management/genai-span-attribute-reference.md) to review the full attribute list.
* Go to [How AI traces work](/cloud-cost-management/references/ai-cost-management/how-ai-traces-work.md) to understand trace attribution in depth.
* Go to [AI Cost Troubleshooting](/cloud-cost-management/resources/ai-cost-troubleshooting.md) if traces do not appear or show no cost.