--- title: "Billing" url: "/docs/proxy/billing" canonical_url: "https://docs.litellm.ai/docs/proxy/billing" type: "docs" last_updated: "2026-10-08" summary: "Bill internal teams, external customers for their usage" related: - "/docs/proxy/spend_capture_rate" - "/docs/proxy/billing_metrics" --- # Billing > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt Bill internal teams, external customers for their usage **🚨 Requirements** - [Setup Lago](https://docs.getlago.com/guide/self-hosted/docker#run-the-app), for usage-based billing. We recommend following [their Stripe tutorial](https://docs.getlago.com/templates/per-transaction/stripe#step-1-create-billable-metrics-for-transaction) Steps: - Connect the proxy to Lago - Set the id you want to bill for (customers, internal users, teams) - Start! ## Quick Start Bill internal teams for their usage ### 1. Connect proxy to Lago Set 'lago' as a callback on your proxy config.yaml ```yaml model_list: - model_name: fake-openai-endpoint litellm_params: model: openai/fake api_key: fake-key api_base: https://exampleopenaiendpoint-production.up.railway.app/ litellm_settings: callbacks: ["lago"] # 👈 KEY CHANGE general_settings: master_key: os.environ/LITELLM_MASTER_KEY ``` Add your Lago keys to the environment ```bash export LAGO_API_BASE="http://localhost:3000" # self-host - https://docs.getlago.com/guide/self-hosted/docker#run-the-app export LAGO_API_KEY="3e29d607-de54-49aa-a019-ecf585729070" # Get key - https://docs.getlago.com/guide/self-hosted/docker#find-your-api-key export LAGO_API_EVENT_CODE="openai_tokens" # name of lago billing code export LAGO_API_CHARGE_BY="team_id" # 👈 Charges 'team_id' attached to proxy key ``` Start proxy ```bash litellm --config /path/to/config.yaml ``` ### 2. Create Key for Internal Team ```bash curl 'http://0.0.0.0:4000/key/generate' \ --header "Authorization: Bearer $LITELLM_API_KEY" \ --header 'Content-Type: application/json' \ --data-raw '{"team_id": "my-unique-id"}' # 👈 Internal Team's ID ``` Response Object: ```bash { "key": "sk-tXL0wt5-lOOVK9sfY2UacA", } ``` ### 3. Start billing! **Curl** ```bash # Authorization: 👈 Team's Key curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer sk-tXL0wt5-lOOVK9sfY2UacA' \ --data ' { "model": "fake-openai-endpoint", "messages": [ { "role": "user", "content": "what llm are you" } ], } ' ``` **OpenAI Python SDK** ```python import openai client = openai.OpenAI( api_key="sk-tXL0wt5-lOOVK9sfY2UacA", # 👈 Team's Key base_url="http://0.0.0.0:4000" ) # request sent to model set on litellm proxy, `litellm --model` response = client.chat.completions.create(model="gpt-5.6-terra", messages = [ { "role": "user", "content": "this is a test request, write a short poem" } ]) print(response) ``` **Langchain** ```python 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-tXL0wt5-lOOVK9sfY2UacA" # 👈 Team's Key chat = ChatOpenAI( openai_api_base="http://0.0.0.0:4000", model = "gpt-5.6-terra", temperature=0.1, ) 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) ``` **See Results on Lago** ## Advanced - Lago Logging object This is what LiteLLM will log to Lagos ``` { "event": { "transaction_id": "", "external_customer_id": , # either 'end_user_id', 'user_id', or 'team_id'. Default 'end_user_id'. "code": os.getenv("LAGO_API_EVENT_CODE"), "properties": { "input_tokens": , "output_tokens": , "model": , "response_cost": , # 👈 LITELLM CALCULATED RESPONSE COST - https://github.com/BerriAI/litellm/blob/d43f75150a65f91f60dc2c0c9462ce3ffc713c1f/litellm/utils.py#L1473 } } } ``` ## Advanced - Bill Customers, Internal Users For: - Customers (id passed via 'user' param in /chat/completion call) = 'end_user_id' - Internal Users (id set when [creating keys](https://docs.litellm.ai/docs/proxy/virtual_keys#advanced---spend-tracking)) = 'user_id' - Teams (id set when [creating keys](https://docs.litellm.ai/docs/proxy/virtual_keys#advanced---spend-tracking)) = 'team_id' **Customer Billing** 1. Set 'LAGO_API_CHARGE_BY' to 'end_user_id' ```bash export LAGO_API_CHARGE_BY="end_user_id" ``` 2. Test it! **Curl** ```shell curl --location 'http://0.0.0.0:4000/chat/completions' \ --header 'Content-Type: application/json' \ --data ' { "model": "gpt-5.6-terra", "messages": [ { "role": "user", "content": "what llm are you" } ], "user": "my_customer_id" # 👈 whatever your customer id is } ' ``` **OpenAI Python SDK** ```python import openai client = openai.OpenAI( api_key="anything", base_url="http://0.0.0.0:4000" ) # request sent to model set on litellm proxy, `litellm --model` response = client.chat.completions.create(model="gpt-5.6-terra", messages = [ { "role": "user", "content": "this is a test request, write a short poem" } ], user="my_customer_id") # 👈 whatever your customer id is print(response) ``` **Langchain** ```python 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"] = "anything" chat = ChatOpenAI( openai_api_base="http://0.0.0.0:4000", model = "gpt-5.6-terra", temperature=0.1, extra_body={ "user": "my_customer_id" # 👈 whatever your customer id is } ) 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) ``` **Internal User Billing** 1. Set 'LAGO_API_CHARGE_BY' to 'user_id' ```bash export LAGO_API_CHARGE_BY="user_id" ``` 2. Create a key for that user ```bash curl 'http://0.0.0.0:4000/key/generate' \ --header 'Authorization: Bearer ' \ --header 'Content-Type: application/json' \ --data-raw '{"user_id": "my-unique-id"}' # 👈 Internal User's id ``` Response Object: ```bash { "key": "sk-tXL0wt5-lOOVK9sfY2UacA", } ``` 3. Make API Calls with that Key ```python import openai client = openai.OpenAI( api_key="sk-tXL0wt5-lOOVK9sfY2UacA", # 👈 Generated key base_url="http://0.0.0.0:4000" ) # request sent to model set on litellm proxy, `litellm --model` response = client.chat.completions.create(model="gpt-5.6-terra", messages = [ { "role": "user", "content": "this is a test request, write a short poem" } ]) print(response) ``` ## Related pages - [Spend Capture Rate](https://docs.litellm.ai/docs/proxy/spend_capture_rate.md) - [Billable Request Metering](https://docs.litellm.ai/docs/proxy/billing_metrics.md)