Cost Tracking

When you use TraceRoot’s auto-instrumentation for OpenAI, Anthropic, or LangChain, token usage and cost are tracked automatically — no extra code needed.

Python
TypeScript

import traceroot
from traceroot import Integration

traceroot.initialize(integrations=[Integration.OPENAI])

# All OpenAI calls are now automatically tracked — tokens, cost, model
client.chat.completions.create(...)

import OpenAI from 'openai';
import { TraceRoot } from '@traceroot-ai/traceroot';

TraceRoot.initialize({ instrumentModules: { openAI: OpenAI } });

// All OpenAI calls are now automatically tracked — tokens, cost, model
await client.chat.completions.create({ ... });

How It Works

Auto-instrumentation — TraceRoot intercepts LLM calls from OpenAI, Anthropic, and LangChain and captures token usage from the API response automatically.
Fallback estimation — When token counts aren’t in the response, TraceRoot estimates using tiktoken.
Cost calculation — Token counts are multiplied by the model’s pricing from TraceRoot’s pricing table.

What Gets Tracked

Each LLM span captures:

Metric	Description
Input tokens	Tokens in the prompt/messages
Output tokens	Tokens in the completion/response
Total tokens	Sum of input and output
Cost (USD)	Calculated from token usage and model pricing

Viewing Costs

In the dashboard, costs are visible at both the span and trace level:

Per span — see the cost of each individual LLM call
Per trace — see the total cost aggregated across all LLM calls in the trace

Manual Token Reporting

For custom or unsupported providers, report token usage manually:

Python
TypeScript

from traceroot import observe, update_current_span

@observe(name="custom_llm", type="llm")
def call_custom_llm(prompt: str):
    response = my_llm.generate(prompt)

    update_current_span(
        model="my-custom-model",
        usage={
            "input_tokens": response.input_token_count,
            "output_tokens": response.output_token_count,
        },
    )

    return response.text

import { observe, updateCurrentSpan } from '@traceroot-ai/traceroot';

const result = await observe({ name: 'custom_llm', type: 'llm' }, async () => {
  const response = await myLlm.generate(prompt);

  updateCurrentSpan({
    metadata: {
      model: 'my-custom-model',
      usage: {
        input_tokens: response.inputTokenCount,
        output_tokens: response.outputTokenCount,
      },
    },
  });

  return response.text;
});

Overview

Tracing

AI Agents

Detectors

CLI

How It Works

What Gets Tracked

Viewing Costs

Manual Token Reporting

​How It Works

​What Gets Tracked

​Viewing Costs

​Manual Token Reporting

How It Works

What Gets Tracked

Viewing Costs

Manual Token Reporting