๐ธ Spend Tracking
Track spend for keys, users, and teams across 100+ LLMs.
LiteLLM automatically tracks spend for all known models. See our model cost map
How to Track Spend with LiteLLMโ
Step 1
๐ Setup LiteLLM with a Database
Step2 Send /chat/completions
request
- OpenAI Python v1.0.0+
- Curl Request
- 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)
Pass metadata
as part of the request body
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)
Step3 - Verify Spend Tracked That's IT. Now Verify your spend was tracked
- Response Headers
- DB + UI
Expect to see x-litellm-response-cost
in the response headers with calculated cost
The following spend gets tracked in Table LiteLLM_SpendLogs
{
"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,
}
Navigate to the Usage Tab on the LiteLLM UI (found on https://your-proxy-endpoint/ui) and verify you see spend tracked under Usage
Allowing Non-Proxy Admins to access /spend
endpointsโ
Use this when you want non-proxy admins to access /spend
endpoints
Create Keyโ
Create Key with with permissions={"get_spend_routes": true}
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}
}'
Use generated key on /spend
endpointsโ
Access spend Routes with newly generate keys
curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30' \
-H 'Authorization: Bearer sk-H16BKvrSNConSsBYLGc_7A'
Reset Team, API Key Spend - MASTER KEY ONLYโ
Use /global/spend/reset
if you want to:
-
Reset the Spend for all API Keys, Teams. The
spend
for ALL Teams and Keys inLiteLLM_TeamTable
andLiteLLM_VerificationToken
will be set tospend=0
-
LiteLLM will maintain all the logs in
LiteLLMSpendLogs
for Auditing Purposes
Requestโ
Only the LITELLM_MASTER_KEY
you set can access this route
curl -X POST \
'http://localhost:4000/global/spend/reset' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json'
Expected Responsesโ
{"message":"Spend for all API Keys and Teams reset successfully","status":"success"}
Total spend per userโ
Assuming you have been issuing keys for end users, and setting their user_id
on the key, you can check their usage.
curl -L -X GET 'http://localhost: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": []
}
Warning
End users can provide the user
parameter in their request bodies, doing this will increment the cost reported via /customer/info?end_user_id=self-declared-user
, and not for the user that owns the key as reported by that API. This means users could "avoid" having their spend tracked, through their method.
This means if you need to track user spend, and are giving end users API keys, you must always set user_id when creating their api keys, and use keys issued for that user every time you're making LLM calls on their behalf in backend services. This will track their spend.
Daily Spend Breakdown APIโ
Retrieve granular daily usage data for a user (by model, provider, and API key) with a single endpoint.
Example Request:
curl -L -X GET 'http://localhost: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 Referenceโ
See our Swagger API for more details on the /user/daily/activity
endpoint
Custom Tagsโ
Requirements:
- Virtual Keys & a database should be set up, see virtual keys
Note: By default, LiteLLM will track User-Agent
as a custom tag for cost tracking. This enables viewing usage for tools like Claude Code, Gemini CLI, etc.
Client-side spend tagโ
- Set on Key
- Set on Team
- OpenAI Python v1.0.0+
- OpenAI JS
- Curl Request
- 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"]
}
}
'
Set extra_body={"metadata": { }}
to metadata
you want to pass
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"] # ๐ 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"], // ๐ Key Change
},
});
console.log(response);
} catch (error) {
console.log("got this exception from server");
console.error(error);
}
}
// Call the asynchronous function
runOpenAI();
Pass metadata
as part of the request body
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)
Add custom headers to spend trackingโ
You can add custom headers to the request to track spend and usage.
litellm_settings:
extra_spend_tag_headers:
- "x-custom-header"
Disable user-agent trackingโ
You can disable user-agent tracking by setting litellm_settings.disable_user_agent_tracking
to true
.
litellm_settings:
disable_user_agent_tracking: true
โจ (Enterprise) Generate Spend Reportsโ
Use this to charge other teams, customers, users
Use the /global/spend/report
endpoint to get spend reports
- Spend Per Team
- Spend Per Customer
- Spend for Specific API Key
- Spend for Internal User (Key Owner)
Example Requestโ
๐ Key Change: Specify group_by=team
curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&group_by=team' \
-H 'Authorization: Bearer sk-1234'
Example Responseโ
- Expected Response
- Script to Parse Response (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 = 'http://localhost: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()
Output from script
# 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}]
Example Requestโ
๐ Key Change: Specify group_by=customer
curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&group_by=customer' \
-H 'Authorization: Bearer sk-1234'
Example Responseโ
[
{
"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
}
]
}
]
}
]
๐ Key Change: Specify api_key=sk-1234
curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30&api_key=sk-1234' \
-H 'Authorization: Bearer sk-1234'
Example Responseโ
[
{
"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
}
]
}
]
Internal User (Key Owner): This is the value of user_id
passed when calling /key/generate
๐ Key Change: Specify internal_user_id=ishaan
curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-12-30&internal_user_id=ishaan' \
-H 'Authorization: Bearer sk-1234'
Example Responseโ
[
{
"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
}
]
}
]
๐ Spend Logs API - Individual Transaction Logsโ
The /spend/logs
endpoint now supports a summarize
parameter to control data format when using date filters.
Key Parametersโ
Parameter | Description |
---|---|
summarize | New parameter: true (default) = aggregated data, false = individual transaction logs |
Examplesโ
Get individual transaction logs:
curl -X GET "http://localhost:4000/spend/logs?start_date=2024-01-01&end_date=2024-01-02&summarize=false" \
-H "Authorization: Bearer sk-1234"
Get summarized data (default):
curl -X GET "http://localhost:4000/spend/logs?start_date=2024-01-01&end_date=2024-01-02" \
-H "Authorization: Bearer sk-1234"
Use Cases:
summarize=false
: Analytics dashboards, ETL processes, detailed audit trailssummarize=true
: Daily spending reports, high-level cost tracking (legacy behavior)
โจ Custom Spend Log metadataโ
Log specific key,value pairs as part of the metadata for a spend log
Logging specific key,value pairs in spend logs metadata is an enterprise feature. See here