💸 费用跟踪
跟踪跨 100+ LLM 的密钥、用户和团队费用。
LiteLLM 自动跟踪所有已知模型的费用。请参阅我们的 模型成本图
如何使用 LiteLLM 跟踪费用
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)
在请求体中传递 metadata
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 - 验证费用是否已跟踪 就这么简单。现在验证您的费用是否已被跟踪
- 响应头
- 数据库 + UI
预期在响应头中看到包含计算成本的 x-litellm-response-cost
以下费用将在表 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“使用情况”选项卡(位于 https://your-proxy-endpoint/ui),并验证您在 Usage
下看到费用被跟踪
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
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费用路由
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 密钥费用 - 仅限主密钥
如果您想,请使用 /global/spend/reset
重置所有 API 密钥、团队的费用。
LiteLLM_TeamTable
和LiteLLM_VerificationToken
中所有团队和密钥的spend
将设置为spend=0
LiteLLM 会将所有日志保存在
LiteLLMSpendLogs
中用于审计目的
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"}
日费用明细 API
通过一个端点检索用户的详细每日使用数据(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 参考
有关 /user/daily/activity
端点的更多详细信息,请参阅我们的 Swagger API
✨ (企业版) 生成费用报告
使用此功能向其他团队、客户、用户收费
使用 /global/spend/report
端点获取费用报告
- 按团队划分的费用
- 按客户划分的费用
- 特定 API 密钥的费用
- 内部用户(密钥所有者)的费用
示例请求
👉 关键更改: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
- 解析响应的脚本 (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
👉 关键更改: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
}
]
}
]
}
]
👉 关键更改: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": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
"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
值
👉 关键更改: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": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b",
"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
}
]
}
]
✨ 自定义费用日志元数据
将特定的键值对记录为费用日志元数据的一部分
在费用日志元数据中记录特定的键值对是一项企业版功能。请参阅此处
✨ 自定义标签
使用自定义标签跟踪费用是一项企业版功能。请参阅此处