Cost Breakdown

LLM API provides real-time cost information for each API request directly in the response's usage object. This allows you to track costs programmatically without needing to query the dashboard.

Response Format

When cost breakdown is enabled, your API responses will include additional cost fields in the usage object:

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "openai/gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 15,
    "total_tokens": 25,
    "cost_usd_total": 0.000125,
    "cost_usd_input": 0.000025,
    "cost_usd_output": 0.0001,
    "cost_usd_cached_input": 0,
    "cost_usd_request": 0,
    "cost_usd_data_storage": 0.00000025
  }
}

Cost Fields

Field	Description
`cost_usd_total`	Total inference cost for the request in USD (excludes storage)
`cost_usd_input`	Cost for input/prompt tokens in USD
`cost_usd_output`	Cost for output/completion tokens in USD
`cost_usd_cached_input`	Cost for cached input tokens in USD (discounted rate)
`cost_usd_request`	Per-request cost in USD (for models with request-based pricing)
`cost_usd_data_storage`	LLM API storage cost in USD ($0.01 per 1M tokens, only when retention enabled)

INFO

Note: cost_usd_total includes only provider/inference costs. Data storage costs (cost_usd_data_storage) are billed separately by LLM API when data retention is enabled in organization policies.

Streaming Responses

Cost information is also available in streaming responses. The cost fields are included in the final usage chunk sent before the [DONE] message:

data: {"id":"chatcmpl-123","object":"chat.completion.chunk","choices":[...],"usage":{"prompt_tokens":10,"completion_tokens":15,"total_tokens":25,"cost_usd_total":0.000125,"cost_usd_input":0.000025,"cost_usd_output":0.0001}}

data: [DONE]

The usage object will include an informational message instead of cost fields:

{
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 15,
    "total_tokens": 25,
    "info": "upgrade to pro to include usd cost breakdown"
  }
}

Example: Tracking Costs in Code

Here's an example of how to track costs programmatically using the cost breakdown feature:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLM_API_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

async function trackCosts() {
  const response = await client.chat.completions.create({
    model: "gpt-4o",
    messages: [{ role: "user", content: "Hello!" }],
  });

  const usage = response.usage as any;

  if (usage.cost_usd_total !== undefined) {
    console.log(`Request cost: ${usage.cost_usd_total.toFixed(6)}`);
    console.log(`  Input: ${usage.cost_usd_input.toFixed(6)}`);
    console.log(`  Output: ${usage.cost_usd_output.toFixed(6)}`);

    if (usage.cost_usd_cached_input > 0) {
      console.log(`  Cached: ${usage.cost_usd_cached_input.toFixed(6)}`);
    }
  }

  return response;
}

Use Cases

Budget Monitoring

Track costs in real-time and implement budget limits in your application:

let totalSpent = 0;
const BUDGET_LIMIT = 10.0; // $10 budget

async function makeRequest(messages: Message[]) {
  const response = await client.chat.completions.create({
    model: "gpt-4o",
    messages,
  });

  const cost = (response.usage as any).cost_usd_total || 0;
  totalSpent += cost;

  if (totalSpent > BUDGET_LIMIT) {
    throw new Error(`Budget exceeded: ${totalSpent.toFixed(2)}`);
  }

  return response;
}

Per-User Cost Allocation

Track costs per user for billing or analytics:

const userCosts: Map<string, number> = new Map();

async function makeRequestForUser(userId: string, messages: Message[]) {
  const response = await client.chat.completions.create({
    model: "gpt-4o",
    messages,
  });

  const cost = (response.usage as any).cost_usd_total || 0;
  const currentCost = userCosts.get(userId) || 0;
  userCosts.set(userId, currentCost + cost);

  return response;
}

Cost Analytics

Aggregate costs by model, time period, or any other dimension:

interface CostEntry {
  timestamp: Date;
  model: string;
  inputCost: number;
  outputCost: number;
  totalCost: number;
}

const costLog: CostEntry[] = [];

async function loggedRequest(model: string, messages: Message[]) {
  const response = await client.chat.completions.create({
    model,
    messages,
  });

  const usage = response.usage as any;

  costLog.push({
    timestamp: new Date(),
    model: response.model,
    inputCost: usage.cost_usd_input || 0,
    outputCost: usage.cost_usd_output || 0,
    totalCost: usage.cost_usd_total || 0,
  });

  return response;
}

Data Storage Costs

When data retention is enabled in organization policies, LLM API stores full request and response payloads for the configured retention period. This storage incurs a small additional cost:

Rate: $0.01 per 1 million tokens
Applies to: Input, cached, output, and reasoning tokens
When charged: Only when retention level is set to "Retain All Data"
Billing mode: In API keys mode, only storage costs are deducted from credits (inference costs are billed to your provider keys)

Storage costs are displayed separately from inference costs in the dashboard and usage breakdown to maintain transparency between provider costs and LLM API platform costs.

SUCCESS

Enable auto top-up in billing settings to prevent request failures when storage costs deplete your credits.