litellm-mirror/docs/my-website/docs/proxy/custom_pricing.md

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Custom LLM Pricing

Use this to register custom pricing for models.

There's 2 ways to track cost:

• cost per token
• cost per second

By default, the response cost is accessible in the logging object via kwargs["response_cost"] on success (sync + async). Learn More

:::info

LiteLLM already has pricing for any model in our model cost map.

:::

Cost Per Second (e.g. Sagemaker)

Usage with LiteLLM Proxy Server

Step 1: Add pricing to config.yaml

model_list:
  - model_name: sagemaker-completion-model
    litellm_params:
      model: sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4
    model_info:
      input_cost_per_second: 0.000420
  - model_name: sagemaker-embedding-model
    litellm_params:
      model: sagemaker/berri-benchmarking-gpt-j-6b-fp16
    model_info:
      input_cost_per_second: 0.000420 

Step 2: Start proxy

litellm /path/to/config.yaml

Step 3: View Spend Logs

<Image img={require('../../img/spend_logs_table.png')} />

Cost Per Token (e.g. Azure)

Usage with LiteLLM Proxy Server

model_list:
  - model_name: azure-model
    litellm_params:
      model: azure/<your_deployment_name>
      api_key: os.environ/AZURE_API_KEY
      api_base: os.environ/AZURE_API_BASE
      api_version: os.envrion/AZURE_API_VERSION
    model_info:
      input_cost_per_token: 0.000421 # 👈 ONLY to track cost per token
      output_cost_per_token: 0.000520 # 👈 ONLY to track cost per token

Override Model Cost Map

You can override our model cost map with your own custom pricing for a mapped model.

Just add a model_info key to your model in the config, and override the desired keys.

Example: Override Anthropic's model cost map for the prod/claude-3-5-sonnet-20241022 model.

model_list:
  - model_name: "prod/claude-3-5-sonnet-20241022"
    litellm_params:
      model: "anthropic/claude-3-5-sonnet-20241022"
      api_key: os.environ/ANTHROPIC_PROD_API_KEY
    model_info:
      input_cost_per_token: 0.000006
      output_cost_per_token: 0.00003
      cache_creation_input_token_cost: 0.0000075
      cache_read_input_token_cost: 0.0000006

Set 'base_model' for Cost Tracking (e.g. Azure deployments)

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

Debugging

If you're custom pricing is not being used or you're seeing errors, please check the following:

1. Run the proxy with LITELLM_LOG="DEBUG" or the --detailed_debug cli flag

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

2. Check logs for this line:

LiteLLM:DEBUG: utils.py:263 - litellm.acompletion

3. Check if 'input_cost_per_token' and 'output_cost_per_token' are top-level keys in the acompletion function.

acompletion(
  ...,
  input_cost_per_token: my-custom-price, 
  output_cost_per_token: my-custom-price,
)

If these keys are not present, LiteLLM will not use your custom pricing.

If the problem persists, please file an issue on GitHub.