消费跟踪
跟踪 100 多个 LLM 的密钥、用户和团队的支出。
LiteLLM 会自动跟踪所有已知模型的支出。请参阅我们的 模型成本图
从 GitHub 同步模型定价数据,以确保准确的成本跟踪。
如何使用 LiteLLM 跟踪支出
步骤 1
步骤 2 发送 /chat/completions 请求
- OpenAI Python v1.0.0+
- Curl 请求
- Langchain
import openai
client = openai.OpenAI(
api_key="sk-1234",
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(
model="llama3",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
user="palantir", # OPTIONAL: pass user to track spend by user
extra_body={
"metadata": {
"tags": ["jobID:214590dsff09fds", "taskName:run_page_classification"] # ENTERPRISE: pass tags to track spend by tags
}
}
)
print(response)
将 metadata 作为请求主体的一部分传递
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-1234' \
--data '{
"model": "llama3",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
"user": "palantir", # OPTIONAL: pass user to track spend by user
"metadata": {
"tags": ["jobID:214590dsff09fds", "taskName:run_page_classification"] # ENTERPRISE: pass tags to track spend by tags
}
}'
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
import os
os.environ["OPENAI_API_KEY"] = "sk-1234"
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000",
model = "llama3",
user="palantir",
extra_body={
"metadata": {
"tags": ["jobID:214590dsff09fds", "taskName:run_page_classification"] # ENTERPRISE: pass tags to track spend by tags
}
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
步骤 3 - 验证跟踪的支出 就这样了。现在验证您的支出是否已被跟踪
- 响应头
- 数据库 + UI
预计会在响应头中看到 x-litellm-response-cost,其中包含计算出的成本
以下支出将在表 LiteLLM_SpendLogs 中被跟踪
{
"api_key": "fe6b0cab4ff5a5a8df823196cc8a450*****", # Hash of API Key used
"user": "default_user", # Internal User (LiteLLM_UserTable) that owns `api_key=sk-1234`.
"team_id": "e8d1460f-846c-45d7-9b43-55f3cc52ac32", # Team (LiteLLM_TeamTable) that owns `api_key=sk-1234`
"request_tags": ["jobID:214590dsff09fds", "taskName:run_page_classification"],# Tags sent in request
"end_user": "palantir", # Customer - the `user` sent in the request
"model_group": "llama3", # "model" passed to LiteLLM
"api_base": "https://api.groq.com/openai/v1/", # "api_base" of model used by LiteLLM
"spend": 0.000002, # Spend in $
"total_tokens": 100,
"completion_tokens": 80,
"prompt_tokens": 20,
}
导航到 LiteLLM UI 上的“用法”选项卡(位于 https://your-proxy-endpoint/ui),并验证您是否在 Usage 下看到跟踪的支出
允许非代理管理员访问 /spend 端点
当您希望非代理管理员访问 /spend 端点时使用此功能
安排一次 与我们的会议以获取您的企业许可证
创建密钥
使用 permissions={"get_spend_routes": true} 创建密钥
curl --location 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
"permissions": {"get_spend_routes": true}
}'
在 /spend 端点上使用生成的密钥
使用新生成的密钥访问支出路由
curl -X GET 'https://:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30' \
-H 'Authorization: Bearer sk-H16BKvrSNConSsBYLGc_7A'
重置团队、API 密钥支出 - 仅 MASTER 密钥
如果您希望,请使用 /global/spend/reset
-
重置所有 API 密钥、团队的支出。
LiteLLM_TeamTable和LiteLLM_VerificationToken中的所有团队和密钥的spend将设置为spend=0 -
LiteLLM 将在
LiteLLMSpendLogs中保留所有日志以供审计
请求
只有您设置的 LITELLM_MASTER_KEY 才能访问此路由
curl -X POST \
'https://:4000/global/spend/reset' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json'
预期响应
{"message":"Spend for all API Keys and Teams reset successfully","status":"success"}
每个用户的总支出
假设您已经为最终用户颁发了密钥,并在密钥上设置了他们的 user_id,您可以检查他们的使用情况。
curl -L -X GET 'https://:4000/user/info?user_id=jane_smith' \
-H 'Authorization: Bearer sk-...'
{
"user_id": "jane_smith",
"user_info": {
"spend": 0.1
},
"keys": [
{
"token": "6e952b0efcafbb6350240db25ed534b4ec6011b3e1ba1006eb4f903461fd36f6",
"key_name": "sk-...KE_A",
"key_alias": "user-01882d6b-e090-776a-a587-21c63e502670-01983ddb-872f-71a3-8b3a-f9452c705483",
"soft_budget_cooldown": false,
"spend": 0.1,
"expires": "2025-07-31T19:14:13.968000+00:00",
"models": [],
"aliases": {},
"config": {},
"user_id": "01982d6b-e090-776a-a587-21c63e502660",
"team_id": "f2044fde-2293-482f-bf35-a8dab4e85c5f",
"permissions": {},
"max_parallel_requests": null,
"metadata": {},
"blocked": null,
"tpm_limit": null,
"rpm_limit": null,
"max_budget": null,
"budget_duration": null,
"budget_reset_at": null,
"allowed_cache_controls": [],
"allowed_routes": [],
"model_spend": {},
"model_max_budget": {},
"budget_id": null,
"organization_id": null,
"object_permission_id": null,
"created_at": "2025-07-24T19:14:13.970000Z",
"created_by": "582b168f-fc11-4e14-ad6a-cf4bb3656ddc",
"updated_at": "2025-07-24T19:14:13.970000Z",
"updated_by": "582b168f-fc11-4e14-ad6a-cf4bb3656ddc",
"litellm_budget_table": null,
"litellm_organization_table": null,
"object_permission": null,
"team_alias": null
}
],
"teams": []
}
警告 最终用户可以在他们的请求主体中提供 user 参数,这样做会增加通过 /customer/info?end_user_id=self-declared-user 报告的成本,而不是通过该 API 报告的拥有该密钥的用户。这意味着用户可以通过他们的这种方法“避免”跟踪他们的支出。这意味着如果您需要跟踪用户支出,并且正在向最终用户提供 API 密钥,则必须始终在创建他们的 api 密钥时设置 user_id,并在后端服务中每次代表他们进行 LLM 调用时使用为该用户发出的密钥。这将跟踪他们的支出。
每日支出分解 API
使用单个端点检索用户的细粒度每日使用数据(按模型、提供商和 API 密钥)。
示例请求
curl -L -X GET 'https://:4000/user/daily/activity?start_date=2025-03-20&end_date=2025-03-27' \
-H 'Authorization: Bearer sk-...'
{
"results": [
{
"date": "2025-03-27",
"metrics": {
"spend": 0.0177072,
"prompt_tokens": 111,
"completion_tokens": 1711,
"total_tokens": 1822,
"api_requests": 11
},
"breakdown": {
"models": {
"gpt-4o-mini": {
"spend": 1.095e-05,
"prompt_tokens": 37,
"completion_tokens": 9,
"total_tokens": 46,
"api_requests": 1
},
"providers": { "openai": { ... }, "azure_ai": { ... } },
"api_keys": { "3126b6eaf1...": { ... } }
}
}
],
"metadata": {
"total_spend": 0.7274667,
"total_prompt_tokens": 280990,
"total_completion_tokens": 376674,
"total_api_requests": 14
}
}
API Reference
请参阅我们的 Swagger API,了解有关 /user/daily/activity 端点的更多详细信息
自定义标签
要求
- 应设置虚拟密钥和数据库,请参阅 虚拟密钥
注意: 默认情况下,LiteLLM 将跟踪 User-Agent 作为成本跟踪的自定义标签。这使得能够查看 Claude Code、Gemini CLI 等工具的使用情况。
客户端支出标签
- 在密钥上设置
- 在团队上设置
- OpenAI Python v1.0.0+
- OpenAI JS
- Curl 请求
- Langchain
curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"metadata": {
"tags": ["tag1", "tag2", "tag3"]
}
}
'
curl -L -X POST 'http://0.0.0.0:4000/team/new' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"metadata": {
"tags": ["tag1", "tag2", "tag3"]
}
}
'
将 extra_body={"metadata": { }} 设置为要传递的 metadata
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_body={
"metadata": {
"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"] # 👈 Key Change
}
}
)
print(response)
const openai = require("openai");
async function runOpenAI() {
const client = new openai.OpenAI({
apiKey: "sk-1234",
baseURL: "http://0.0.0.0:4000",
});
try {
const response = await client.chat.completions.create({
model: "gpt-3.5-turbo",
messages: [
{
role: "user",
content: "this is a test request, write a short poem",
},
],
metadata: {
tags: ["model-anthropic-claude-v2.1", "app-ishaan-prod"], // 👈 Key Change
},
});
console.log(response);
} catch (error) {
console.log("got this exception from server");
console.error(error);
}
}
// Call the asynchronous function
runOpenAI();
将 metadata 作为请求主体的一部分传递
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
"metadata": {"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"]}
}'
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000",
model = "gpt-3.5-turbo",
temperature=0.1,
extra_body={
"metadata": {
"tags": ["model-anthropic-claude-v2.1", "app-ishaan-prod"]
}
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
为支出跟踪添加自定义标头
您可以添加自定义标头到请求中以跟踪支出和使用情况。
litellm_settings:
extra_spend_tag_headers:
- "x-custom-header"
禁用 user-agent 跟踪
您可以通过将 litellm_settings.disable_add_user_agent_to_request_tags 设置为 true 来禁用 user-agent 跟踪。
litellm_settings:
disable_add_user_agent_to_request_tags: true
✨ (企业版) 生成支出报告
用于向其他团队、客户、用户收费
使用 /global/spend/report 端点获取支出报告
- 按团队的支出
- 按客户的支出
- 特定 API 密钥的支出
- 内部用户(密钥所有者)的支出
示例请求
👉 密钥更改:指定 group_by=team
curl -X GET 'https://:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&group_by=team' \
-H 'Authorization: Bearer sk-1234'
示例响应
- 预期响应
- 脚本解析响应(Python)
[
{
"group_by_day": "2024-04-30T00:00:00+00:00",
"teams": [
{
"team_name": "Prod Team",
"total_spend": 0.0015265,
"metadata": [ # see the spend by unique(key + model)
{
"model": "gpt-4",
"spend": 0.00123,
"total_tokens": 28,
"api_key": "88dc28.." # the hashed api key
},
{
"model": "gpt-4",
"spend": 0.00123,
"total_tokens": 28,
"api_key": "a73dc2.." # the hashed api key
},
{
"model": "chatgpt-v-2",
"spend": 0.000214,
"total_tokens": 122,
"api_key": "898c28.." # the hashed api key
},
{
"model": "gpt-3.5-turbo",
"spend": 0.0000825,
"total_tokens": 85,
"api_key": "84dc28.." # the hashed api key
}
]
}
]
}
]
import requests
url = 'https://:4000/global/spend/report'
params = {
'start_date': '2023-04-01',
'end_date': '2024-06-30'
}
headers = {
'Authorization': 'Bearer sk-1234'
}
# Make the GET request
response = requests.get(url, headers=headers, params=params)
spend_report = response.json()
for row in spend_report:
date = row["group_by_day"]
teams = row["teams"]
for team in teams:
team_name = team["team_name"]
total_spend = team["total_spend"]
metadata = team["metadata"]
print(f"Date: {date}")
print(f"Team: {team_name}")
print(f"Total Spend: {total_spend}")
print("Metadata: ", metadata)
print()
脚本输出
# Date: 2024-05-11T00:00:00+00:00
# Team: local_test_team
# Total Spend: 0.003675099999999999
# Metadata: [{'model': 'gpt-3.5-turbo', 'spend': 0.003675099999999999, 'api_key': 'b94d5e0bc3a71a573917fe1335dc0c14728c7016337451af9714924ff3a729db', 'total_tokens': 3105}]
# Date: 2024-05-13T00:00:00+00:00
# Team: Unassigned Team
# Total Spend: 3.4e-05
# Metadata: [{'model': 'gpt-3.5-turbo', 'spend': 3.4e-05, 'api_key': '9569d13c9777dba68096dea49b0b03e0aaf4d2b65d4030eda9e8a2733c3cd6e0', 'total_tokens': 50}]
# Date: 2024-05-13T00:00:00+00:00
# Team: central
# Total Spend: 0.000684
# Metadata: [{'model': 'gpt-3.5-turbo', 'spend': 0.000684, 'api_key': '0323facdf3af551594017b9ef162434a9b9a8ca1bbd9ccbd9d6ce173b1015605', 'total_tokens': 498}]
# Date: 2024-05-13T00:00:00+00:00
# Team: local_test_team
# Total Spend: 0.0005715000000000001
# Metadata: [{'model': 'gpt-3.5-turbo', 'spend': 0.0005715000000000001, 'api_key': 'b94d5e0bc3a71a573917fe1335dc0c14728c7016337451af9714924ff3a729db', 'total_tokens': 423}]
示例请求
👉 密钥更改:指定 group_by=customer
curl -X GET 'https://:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&group_by=customer' \
-H 'Authorization: Bearer sk-1234'
示例响应
[
{
"group_by_day": "2024-04-30T00:00:00+00:00",
"customers": [
{
"customer": "palantir",
"total_spend": 0.0015265,
"metadata": [ # see the spend by unique(key + model)
{
"model": "gpt-4",
"spend": 0.00123,
"total_tokens": 28,
"api_key": "88dc28.." # the hashed api key
},
{
"model": "gpt-4",
"spend": 0.00123,
"total_tokens": 28,
"api_key": "a73dc2.." # the hashed api key
},
{
"model": "chatgpt-v-2",
"spend": 0.000214,
"total_tokens": 122,
"api_key": "898c28.." # the hashed api key
},
{
"model": "gpt-3.5-turbo",
"spend": 0.0000825,
"total_tokens": 85,
"api_key": "84dc28.." # the hashed api key
}
]
}
]
}
]
👉 密钥更改:指定 api_key=sk-1234
curl -X GET 'https://:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&api_key=sk-1234' \
-H 'Authorization: Bearer sk-1234'
示例响应
[
{
"api_key": "example-api-key-123",
"total_cost": 0.3201286305151999,
"total_input_tokens": 36.0,
"total_output_tokens": 1593.0,
"model_details": [
{
"model": "dall-e-3",
"total_cost": 0.31999939051519993,
"total_input_tokens": 0,
"total_output_tokens": 0
},
{
"model": "llama3-8b-8192",
"total_cost": 0.00012924,
"total_input_tokens": 36,
"total_output_tokens": 1593
}
]
}
]
内部用户(密钥所有者):这是调用 /key/generate 时传递的 user_id 的值
👉 密钥更改:指定 internal_user_id=ishaan
curl -X GET 'https://:4000/global/spend/report?start_date=2024-04-01&end_date=2024-12-30&internal_user_id=ishaan' \
-H 'Authorization: Bearer sk-1234'
示例响应
[
{
"api_key": "example-api-key-123",
"total_cost": 0.00013132,
"total_input_tokens": 105.0,
"total_output_tokens": 872.0,
"model_details": [
{
"model": "gpt-3.5-turbo-instruct",
"total_cost": 5.85e-05,
"total_input_tokens": 15,
"total_output_tokens": 18
},
{
"model": "llama3-8b-8192",
"total_cost": 7.282000000000001e-05,
"total_input_tokens": 90,
"total_output_tokens": 854
}
]
},
{
"api_key": "151e85e46ab8c9c7fad090793e3fe87940213f6ae665b543ca633b0b85ba6dc6",
"total_cost": 5.2699999999999993e-05,
"total_input_tokens": 26.0,
"total_output_tokens": 27.0,
"model_details": [
{
"model": "gpt-3.5-turbo",
"total_cost": 5.2499999999999995e-05,
"total_input_tokens": 24,
"total_output_tokens": 27
},
{
"model": "text-embedding-ada-002",
"total_cost": 2e-07,
"total_input_tokens": 2,
"total_output_tokens": 0
}
]
},
{
"api_key": "60cb83a2dcbf13531bd27a25f83546ecdb25a1a6deebe62d007999dc00e1e32a",
"total_cost": 9.42e-06,
"total_input_tokens": 30.0,
"total_output_tokens": 99.0,
"model_details": [
{
"model": "llama3-8b-8192",
"total_cost": 9.42e-06,
"total_input_tokens": 30,
"total_output_tokens": 99
}
]
}
]
📊 支出日志 API - 个人交易日志
/spend/logs 端点现在支持一个 summarize 参数,用于在使用日期过滤器时控制数据格式。
关键参数
| 参数 | 描述 |
|---|---|
summarize | 新参数:true(默认)= 聚合数据,false = 个人交易日志 |
示例
获取个人交易日志
curl -X GET "https://:4000/spend/logs?start_date=2024-01-01&end_date=2024-01-02&summarize=false" \
-H "Authorization: Bearer sk-1234"
获取汇总数据(默认)
curl -X GET "https://:4000/spend/logs?start_date=2024-01-01&end_date=2024-01-02" \
-H "Authorization: Bearer sk-1234"
用例
summarize=false:分析仪表板、ETL 流程、详细审计跟踪summarize=true:每日支出报告、高级成本跟踪(旧行为)
✨ 自定义支出日志元数据
将特定的键值对记录为支出日志元数据的一部分
记录支出日志元数据中的特定键值对是企业功能。
要求
- 应设置虚拟密钥和数据库,请参阅 虚拟密钥
用法 - 带有特殊支出日志元数据的 /chat/completions 请求
- 在密钥上设置
- 在团队上设置
- OpenAI Python v1.0.0+
- OpenAI JS
- Curl 请求
- 使用标头
- Langchain
curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"metadata": {
"spend_logs_metadata": {
"hello": "world"
}
}
}
'
curl -L -X POST 'http://0.0.0.0:4000/team/new' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"metadata": {
"spend_logs_metadata": {
"hello": "world"
}
}
}
'
将 extra_body={"metadata": { }} 设置为要传递的 metadata
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_body={
"metadata": {
"spend_logs_metadata": {
"hello": "world"
}
}
}
)
print(response)
使用标头
import openai
client = openai.OpenAI(
api_key="sk-1234",
base_url="http://0.0.0.0:4000"
)
# Pass spend logs metadata via headers
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_headers={
"x-litellm-spend-logs-metadata": '{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}'
}
)
print(response)
const openai = require('openai');
async function runOpenAI() {
const client = new openai.OpenAI({
apiKey: 'sk-1234',
baseURL: 'http://0.0.0.0:4000'
});
try {
const response = await client.chat.completions.create({
model: 'gpt-3.5-turbo',
messages: [
{
role: 'user',
content: "this is a test request, write a short poem"
},
],
metadata: {
spend_logs_metadata: { // 👈 Key Change
hello: "world"
}
}
});
console.log(response);
} catch (error) {
console.log("got this exception from server");
console.error(error);
}
}
// Call the asynchronous function
runOpenAI();
使用标头
const openai = require('openai');
async function runOpenAI() {
const client = new openai.OpenAI({
apiKey: 'sk-1234',
baseURL: 'http://0.0.0.0:4000'
});
try {
const response = await client.chat.completions.create({
model: 'gpt-3.5-turbo',
messages: [
{
role: 'user',
content: "this is a test request, write a short poem"
},
]
}, {
headers: {
'x-litellm-spend-logs-metadata': '{"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}'
}
});
console.log(response);
} catch (error) {
console.log("got this exception from server");
console.error(error);
}
}
// Call the asynchronous function
runOpenAI();
将 metadata 作为请求主体的一部分传递
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
"metadata": {
"spend_logs_metadata": {
"hello": "world"
}
}
}'
将 x-litellm-spend-logs-metadata 作为带有 JSON 字符串的请求标头传递
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-1234' \
--header 'x-litellm-spend-logs-metadata: {"user_id": "12345", "project_id": "proj_abc", "request_type": "chat_completion"}' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000",
model = "gpt-3.5-turbo",
temperature=0.1,
extra_body={
"metadata": {
"spend_logs_metadata": {
"hello": "world"
}
}
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
查看带有自定义元数据的支出
/spend/logs 请求格式
curl -X GET "http://0.0.0.0:4000/spend/logs?request_id=<your-call-id" \ # e.g.: chatcmpl-9ZKMURhVYSi9D6r6PJ9vLcayIK0Vm
-H "Authorization: Bearer sk-1234"
/spend/logs 响应格式
[
{
"request_id": "chatcmpl-9ZKMURhVYSi9D6r6PJ9vLcayIK0Vm",
"call_type": "acompletion",
"metadata": {
"user_api_key": "example-api-key-123",
"user_api_key_alias": null,
"spend_logs_metadata": { # 👈 LOGGED CUSTOM METADATA
"hello": "world"
},
"user_api_key_team_id": null,
"user_api_key_user_id": "116544810872468347480",
"user_api_key_team_alias": null
},
}
]