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Custom LLM Pricing

Overview​

LiteLLM provides flexible cost tracking and pricing customization for all LLM providers:

  • Custom Pricing - Override default model costs or set pricing for custom models
  • Cost Per Token - Track costs based on input/output tokens (most common)
  • Cost Per Second - Track costs based on runtime (e.g., Sagemaker)
  • Zero-Cost Models - Bypass budget checks for free/on-premises models by setting costs to 0
  • Provider Discounts - Apply percentage-based discounts to specific providers
  • Provider Margins - Add fees/margins to LLM costs for internal billing
  • Base Model Mapping - Ensure accurate cost tracking for Azure deployments

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 100+ models 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

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.environ/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

Additional Cost Keys​

There are other keys you can use to specify costs for different scenarios and modalities:

  • input_cost_per_token_above_200k_tokens - Cost for input tokens when context exceeds 200k tokens
  • output_cost_per_token_above_200k_tokens - Cost for output tokens when context exceeds 200k tokens
  • cache_creation_input_token_cost_above_200k_tokens - Cache creation cost for large contexts
  • cache_read_input_token_cost_above_200k_token - Cache read cost for large contexts
  • input_cost_per_image - Cost per image in multimodal requests
  • output_cost_per_reasoning_token - Cost for reasoning tokens (e.g., OpenAI o1 models)
  • input_cost_per_audio_token - Cost for audio input tokens
  • output_cost_per_audio_token - Cost for audio output tokens
  • input_cost_per_video_per_second - Cost per second of video input
  • input_cost_per_video_per_second_above_128k_tokens - Video cost for large contexts
  • input_cost_per_character - Character-based pricing for some providers

These keys evolve based on how new models handle multimodality. The latest version can be found at https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json.

Zero-Cost Models (Bypass Budget Checks)​

Use Case: You have on-premises or free models that should be accessible even when users exceed their budget limits.

Solution ✅: Set both input_cost_per_token and output_cost_per_token to 0 (explicitly) to bypass all budget checks for that model.

info

When a model is configured with zero cost, LiteLLM will automatically skip ALL budget checks (user, team, team member, end-user, organization, and global proxy budget) for requests to that model.

Important: Both costs must be explicitly set to 0. If costs are null or undefined, the model will be treated as having cost and budget checks will apply.

Configuration Example​

model_list:
  # On-premises model - free to use
  - model_name: on-prem-llama
    litellm_params:
      model: ollama/llama3
      api_base: http://localhost:11434
    model_info:
      input_cost_per_token: 0   # 👈 Explicitly set to 0
      output_cost_per_token: 0  # 👈 Explicitly set to 0
  
  # Paid cloud model - budget checks apply
  - model_name: gpt-4
    litellm_params:
      model: gpt-4
      api_key: os.environ/OPENAI_API_KEY
    # No model_info - uses default pricing from cost map

Behavior​

With the above configuration:

  • User over budget → Can still use on-prem-llama ✅, but blocked from gpt-4 ❌
  • Team over budget → Can still use on-prem-llama ✅, but blocked from gpt-4 ❌
  • End-user over budget → Can still use on-prem-llama ✅, but blocked from gpt-4 ❌

This ensures your free/on-premises models remain accessible regardless of budget constraints, while paid models are still properly governed.

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
  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.