💸 Spend Tracking

Track spend for keys, users, and teams across 100+ LLMs.

Getting Spend Reports - To Charge Other Teams, API Keys

Use the /global/spend/report endpoint to get daily spend per team, with a breakdown of spend per API Key, Model

Example Request

curl -X GET 'http://localhost:4000/global/spend/report?start_date=2024-04-01&end_date=2024-06-30' \
  -H 'Authorization: Bearer sk-1234'

Example Response

Expected Response

[
    {
        "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
                    }
                ]
            }
        ]
    }
]

Script to Parse Response (Python)

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}]

Reset Team, API Key Spend - MASTER KEY ONLY

Use /global/spend/reset if you want to:

• Reset the Spend for all API Keys, Teams. The spend for ALL Teams and Keys in LiteLLM_TeamTable and LiteLLM_VerificationToken will be set to spend=0

• LiteLLM will maintain all the logs in LiteLLMSpendLogs for Auditing Purposes

Request

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 Responses

{"message":"Spend for all API Keys and Teams reset successfully","status":"success"}

Spend Tracking for Azure

Set base model for cost tracking azure image-gen call

Image Generation

model_list: 
  - model_name: dall-e-3
    litellm_params:
        model: azure/dall-e-3-test
        api_version: 2023-06-01-preview
        api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
        api_key: os.environ/AZURE_API_KEY
        base_model: dall-e-3 # 👈 set dall-e-3 as base model
    model_info:
        mode: image_generation

Chat Completions / Embeddings

Problem: Azure returns gpt-4 in the response when azure/gpt-4-1106-preview is used. This leads to inaccurate cost tracking

Solution ✅ : Set base_model on your config so litellm uses the correct model for calculating azure cost

Get the base model name from here

Example config with base_model

model_list:
  - model_name: azure-gpt-3.5
    litellm_params:
      model: azure/chatgpt-v-2
      api_base: os.environ/AZURE_API_BASE
      api_key: os.environ/AZURE_API_KEY
      api_version: "2023-07-01-preview"
    model_info:
      base_model: azure/gpt-4-1106-preview