> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/openlit/openlit/llms.txt
> Use this file to discover all available pages before exploring further.

# Cost tracking

> Automatic per-call LLM cost calculation with support for custom pricing for fine-tuned and private models.

OpenLIT automatically calculates the cost of every LLM API call and records it as a span attribute (`gen_ai.usage.cost`) and an OpenTelemetry metric. No configuration is needed for standard models — cost tracking is on by default.

## How it works

When your instrumented code calls an LLM, OpenLIT:

1. Reads the token usage from the API response (`prompt_tokens`, `completion_tokens`).
2. Looks up the per-token price for that model from the built-in pricing database.
3. Calculates the cost in USD and attaches it to the span and metric.

The built-in pricing database covers all major providers (OpenAI, Anthropic, Google, AWS Bedrock, and more) and is updated regularly.

## Default pricing source

By default, OpenLIT fetches pricing from:

```
https://raw.githubusercontent.com/openlit/openlit/main/assets/pricing.json
```

This file is updated when providers change their rates. You do not need to do anything to use it.

## Custom pricing for fine-tuned or private models

If you use a fine-tuned model or a model not in the default pricing database, specify a custom pricing file via the `pricing_json` parameter:

```python theme={null}
import openlit

# Load pricing from a local file
openlit.init(
    otlp_endpoint="http://127.0.0.1:4318",
    pricing_json="/path/to/custom-pricing.json",
)
```

```python theme={null}
import openlit

# Load pricing from a remote URL
openlit.init(
    otlp_endpoint="http://127.0.0.1:4318",
    pricing_json="https://example.com/pricing.json",
)
```

You can also set it via environment variable:

```bash theme={null}
export OPENLIT_PRICING_JSON=/path/to/custom-pricing.json
```

## Pricing JSON format

Your custom pricing file must follow the same schema as the [default pricing file](https://raw.githubusercontent.com/openlit/openlit/main/assets/pricing.json). The structure groups models by provider and specifies costs in USD per 1,000 tokens:

```json theme={null}
{
  "chat": {
    "my-fine-tuned-gpt4": {
      "promptPrice": 0.03,
      "completionPrice": 0.06
    },
    "my-internal-model": {
      "promptPrice": 0.005,
      "completionPrice": 0.015
    }
  },
  "embedding": {
    "my-embedding-model": {
      "promptPrice": 0.0001
    }
  }
}
```

| Field             | Type    | Description                                      |
| ----------------- | ------- | ------------------------------------------------ |
| `promptPrice`     | `float` | Cost per 1,000 input (prompt) tokens in USD      |
| `completionPrice` | `float` | Cost per 1,000 output (completion) tokens in USD |

<Note>
  If a model is not found in your custom pricing file, OpenLIT falls back to the default pricing database. If the model is not found there either, the `gen_ai.usage.cost` attribute is omitted from the span.
</Note>

## Disable cost tracking

To disable all metrics collection, including cost tracking:

```python theme={null}
import openlit

openlit.init(disable_metrics=True)
```

## Cost in traces

Cost appears as the `gen_ai.usage.cost` span attribute on every instrumented LLM call. You can filter, group, and aggregate this attribute in any OTel-compatible backend to build cost dashboards, set alerts, or attribute costs to specific users or features.

<Tip>
  Use the `service_name` and `environment` parameters in `openlit.init()` to segment costs by application and deployment stage (development vs. production) in your observability backend.
</Tip>

<CardGroup cols={3}>
  <Card title="Configuration" href="/sdk/configuration" icon="sliders">
    Full openlit.init() parameter reference including pricing\_json
  </Card>

  <Card title="Tracing" href="/sdk/features/tracing" icon="chart-line">
    All span attributes captured per LLM call
  </Card>

  <Card title="Destinations" href="/sdk/destinations/overview" icon="link">
    Send cost metrics to Datadog, Grafana, New Relic, and more
  </Card>
</CardGroup>