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💸 Spend Tracking

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

How to Track Spend with LiteLLM​

Step 1

👉 Setup LiteLLM with a Database

Step2 Send /chat/completions request

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",
    extra_body={
        "metadata": {
            "tags": ["jobID:214590dsff09fds", "taskName:run_page_classification"]
        }
    }
)

print(response)

Step3 - Verify Spend Tracked That's IT. Now Verify your spend was tracked

Expect to see x-litellm-response-cost in the response headers with calculated cost

✨ (Enterprise) API Endpoints to get Spend​

Getting Spend Reports - To Charge Other Teams, Customers, Users​

Use the /global/spend/report endpoint to get spend reports

Example Request​

👉 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 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
                    }
                ]
            }
        ]
    }
]

Allowing Non-Proxy Admins to access /spend endpoints​

Use this when you want non-proxy admins to access /spend endpoints

info

Schedule a meeting with us to get your Enterprise License

Create Key​

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 endpoints​

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 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 OpenAI Models​

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

Custom Input/Output Pricing​

👉 Head to Custom Input/Output Pricing to setup custom pricing or your models

✨ Custom k,v pairs​

Log specific key,value pairs as part of the metadata for a spend log

info

Logging specific key,value pairs in spend logs metadata is an enterprise feature. See here

✨ Custom Tags​

info

Tracking spend with Custom tags is an enterprise feature. See here