💰 Setting Team Budgets

Track spend, set budgets for your Internal Team

Setting Monthly Team Budgets

1. Create a team

• Set max_budget=000000001 ($ value the team is allowed to spend)
• Set budget_duration="1d" (How frequently the budget should update)

API

Create a new team and set max_budget and budget_duration

curl -X POST 'http://0.0.0.0:4000/team/new' \
     -H 'Authorization: Bearer sk-1234' \
     -H 'Content-Type: application/json' \
     -d '{
            "team_alias": "QA Prod Bot", 
            "max_budget": 0.000000001, 
            "budget_duration": "1d"
        }' 

Response

{
 "team_alias": "QA Prod Bot",
 "team_id": "de35b29e-6ca8-4f47-b804-2b79d07aa99a",
 "max_budget": 0.0001,
 "budget_duration": "1d",
 "budget_reset_at": "2024-06-14T22:48:36.594000Z"
}  

Admin UI

Possible values for budget_duration

budget_duration	When Budget will reset
budget_duration="1s"	every 1 second
budget_duration="1m"	every 1 min
budget_duration="1h"	every 1 hour
budget_duration="1d"	every 1 day
budget_duration="1mo"	every 1 month

2. Create a key for the team

Create a key for Team=QA Prod Bot and team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a" from Step 1

API

💡 The Budget for Team="QA Prod Bot" budget will apply to this team

curl -X POST 'http://0.0.0.0:4000/key/generate' \
     -H 'Authorization: Bearer sk-1234' \
     -H 'Content-Type: application/json' \
     -d '{"team_id": "de35b29e-6ca8-4f47-b804-2b79d07aa99a"}'

Response

{"team_id":"de35b29e-6ca8-4f47-b804-2b79d07aa99a", "key":"sk-5qtncoYjzRcxMM4bDRktNQ"}

Admin UI

3. Test It

Use the key from step 2 and run this Request twice

API

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
     -H 'Authorization: Bearer sk-mso-JSykEGri86KyOvgxBw' \
     -H 'Content-Type: application/json' \
     -d ' {
           "model": "llama3",
           "messages": [
             {
               "role": "user",
               "content": "hi"
             }
           ]
         }'

On the 2nd response - expect to see the following exception

{
 "error": {
   "message": "Budget has been exceeded! Current cost: 3.5e-06, Max budget: 1e-09",
   "type": "auth_error",
   "param": null,
   "code": 400
 }
}

Admin UI

Advanced

Prometheus metrics for remaining_budget

More info about Prometheus metrics here

You'll need the following in your proxy config.yaml

litellm_settings:
  success_callback: ["prometheus"]
  failure_callback: ["prometheus"]

Expect to see this metric on prometheus to track the Remaining Budget for the team

litellm_remaining_team_budget_metric{team_alias="QA Prod Bot",team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a"} 9.699999999999992e-06

Dynamic TPM/RPM Allocation

Prevent projects from gobbling too much tpm/rpm.

Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. See Code

1. Setup config.yaml

model_list: 
  - model_name: my-fake-model
    litellm_params:
      model: gpt-3.5-turbo
      api_key: my-fake-key
      mock_response: hello-world
      tpm: 60

litellm_settings: 
  callbacks: ["dynamic_rate_limiter"]

general_settings:
  master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
  database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env

2. Start proxy

litellm --config /path/to/config.yaml

3. Test it!

"""
- Run 2 concurrent teams calling same model
- model has 60 TPM
- Mock response returns 30 total tokens / request
- Each team will only be able to make 1 request per minute
"""

import requests
from openai import OpenAI, RateLimitError

def create_key(api_key: str, base_url: str): 
    response = requests.post(
        url="{}/key/generate".format(base_url), 
        json={},
        headers={
            "Authorization": "Bearer {}".format(api_key)
        }
    )

    _response = response.json()

    return _response["key"]

key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")

# call proxy with key 1 - works
openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000")

response = openai_client_1.chat.completions.with_raw_response.create(
    model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
)

print("Headers for call 1 - {}".format(response.headers))
_response = response.parse()
print("Total tokens for call - {}".format(_response.usage.total_tokens))


# call proxy with key 2 -  works 
openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000")

response = openai_client_2.chat.completions.with_raw_response.create(
    model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
)

print("Headers for call 2 - {}".format(response.headers))
_response = response.parse()
print("Total tokens for call - {}".format(_response.usage.total_tokens))
# call proxy with key 2 -  fails
try:  
    openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}])
    raise Exception("This should have failed!")
except RateLimitError as e: 
    print("This was rate limited b/c - {}".format(str(e)))

Expected Response

This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key=<hashed_token> over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}}

✨ [BETA] Set Priority / Reserve Quota

Reserve tpm/rpm capacity for projects in prod.

tip

Reserving tpm/rpm on keys based on priority is a premium feature. Please get an enterprise license for it.

1. Setup config.yaml

model_list:
  - model_name: gpt-3.5-turbo             
    litellm_params:
      model: "gpt-3.5-turbo"       
      api_key: os.environ/OPENAI_API_KEY 
      rpm: 100   

litellm_settings:
  callbacks: ["dynamic_rate_limiter"]
  priority_reservation: {"dev": 0, "prod": 1}

general_settings:
  master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
  database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env

priority_reservation:

• Dict[str, float]
  • str: can be any string
  • float: from 0 to 1. Specify the % of tpm/rpm to reserve for keys of this priority.

Start Proxy

litellm --config /path/to/config.yaml

2. Create a key with that priority

curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer <your-master-key>' \
-H 'Content-Type: application/json' \
-D '{
    "metadata": {"priority": "dev"} # 👈 KEY CHANGE
}'

Expected Response

{
  ...
  "key": "sk-.."
}

3. Test it!

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: sk-...' \ # 👈 key from step 2.
  -D '{
  "model": "gpt-3.5-turbo",
  "messages": [
      {
      "role": "user",
      "content": "what llm are you"
      }
  ],
}'

Expected Response

Key=... over available RPM=0. Model RPM=100, Active keys=None