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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
      input_cost_per_second: 0.000420
  - model_name: sagemaker-embedding-model
    litellm_params:
      model: sagemaker/berri-benchmarking-gpt-j-6b-fp16
      input_cost_per_second: 0.000420

Step 2: Start proxy

litellm /path/to/config.yaml

Step 3: View Spend Logs

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
      input_cost_per_token: 0.000421 # 👈 ONLY to track cost per token
      output_cost_per_token: 0.000520 # 👈 ONLY to track cost per token

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
  1. Check logs for this line: 
LiteLLM:DEBUG: utils.py:263 - litellm.acompletion
  1. 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.