Expose Per-Invocation Cost In LlmResponse: A Guide

In the ever-evolving landscape of Large Language Models (LLMs), managing and understanding costs associated with each invocation is paramount. This article delves into the intricacies of exposing per-invocation costs in LlmResponse, particularly for providers like LiteLLM, and explores how this enhancement can benefit developers and applications alike. By standardizing cost exposure, we aim to provide a more transparent and efficient way to track and manage expenses across different LLM providers.

The Problem: Lack of Standardized Cost Exposure

Currently, the ADK (AI Development Kit) exposes token usage through LlmResponse.usage_metadata, which is a valuable metric. However, it falls short of providing the actual cost incurred per call. For services like LiteLLM and other providers, a computed cost is often available immediately after each invocation. For instance, LiteLLM offers methods like completion_cost(response) or stores the cost in _hidden_params["response_cost"]. The challenge arises because ADK does not natively surface this cost information.

This limitation forces developers into suboptimal solutions:

1. Manual Cost Recomputation: Developers must maintain price sheets and recompute costs from tokens. This approach is not only cumbersome but also introduces the risk of discrepancies and inconsistencies due to potential drifts in pricing models.
2. Direct Patching of Adapters: Another workaround involves patching adapters to directly extract provider costs. This method, while effective, is not scalable or maintainable in the long run. It also tightly couples the application with specific provider implementations, reducing flexibility and portability.

These issues highlight the need for a standardized approach to expose per-invocation costs, making it easier for applications to track and manage their LLM expenses accurately.

The Solution: Standardizing Cost Metadata in LlmResponse

To address the challenges mentioned above, a proposed solution is to add an optional, typed field to LlmResponse. This field would standardize cost exposure across various providers. The suggested structure is as follows:

• LlmResponse.cost_metadata: Optional[CostMetadata]
  • total_cost_usd: Optional[float]
  • prompt_cost_usd: Optional[float]
  • output_cost_usd: Optional[float]
  • currency: Optional[str] (default “USD” when known)
  • provider: Optional[str] (e.g., “litellm”, “vertexai”, “openai”)
  • source: Optional[Literal['provider','adapter','computed']]
  • raw: Optional[dict] (provider-specific passthrough like response_cost)

This structured approach offers several benefits:

• Standardization: It provides a consistent way to access cost information, regardless of the LLM provider.
• Transparency: It offers a clear breakdown of costs, including total cost, prompt cost, and output cost, facilitating better cost management.
• Flexibility: The inclusion of provider-specific raw data allows for capturing additional cost details that may be unique to certain providers.

Population Strategy

To effectively implement this solution, a well-defined population strategy is crucial. Here’s how cost metadata can be populated for different providers:

LiteLLM

• Non-streaming: Prioritize using litellm.completion_cost(response). If this is not available, fall back to response._hidden_params['response_cost'] when present.
• Streaming: Read cost information from the stream wrapper if exposed. If not available, leave the cost metadata unset to avoid silent recomputation.

Other Providers

• If the SDK (Software Development Kit) exposes cost information, pass it directly into cost_metadata. If cost information is not available, set cost_metadata to None.

Example of Cost Metadata

To illustrate how cost metadata would look in practice, consider the following example:

"cost_metadata": {
  "total_cost_usd": 0.00123,
  "prompt_cost_usd": 0.00040,
  "output_cost_usd": 0.00083,
  "currency": "USD",
  "provider": "litellm",
  "source": "provider",
  "raw": { "response_cost": 0.00123 }
}

This JSON snippet provides a comprehensive view of the cost incurred for a specific LLM invocation, including the total cost, breakdown of prompt and output costs, currency, provider, source of the cost data, and raw provider-specific information.

Alternatives Considered

Before proposing the standardization of cost metadata, alternative solutions were evaluated. These alternatives and their drawbacks are discussed below:

Client-Side Recomputation Using Usage Metadata

One alternative is to recompute costs on the client side using usage_metadata and custom price sheets. While this approach is feasible, it presents several challenges:

• Price Maintenance: It requires developers to maintain up-to-date price sheets, which can be time-consuming and error-prone.
• Risk of Drift: Price sheets may not always align perfectly with the provider's pricing models, leading to discrepancies.
• Provider Inconsistency: Results may vary across providers due to differences in tokenization and pricing structures.

Stuffing Cost into Custom Metadata

Another alternative is to store cost information in custom_metadata. However, this approach has limitations:

• Discoverability: Cost information stored in custom_metadata is not easily discoverable or typed, making it harder for applications to rely on it consistently.
• Scalability: It lacks the structure and standardization needed for large-scale applications that interact with multiple LLM providers.

Implementation Touch Points

To effectively implement the proposed solution, several touch points within the ADK need to be addressed. These include:

1. src/google/adk/models/llm_response.py: This file needs to be modified to add the CostMetadata class and the cost_metadata field to the LlmResponse class.
2. src/google/adk/models/lite_llm.py: This file should be updated to populate the cost_metadata field for LiteLLM, considering both non-streaming and streaming scenarios.
3. (Optional) plugins/logging_plugin.py: This file can be modified to log cost information if it is present in the cost_metadata.
4. (Optional) telemetry/tracing.py: This file can be updated to emit span attributes related to cost, such as gen_ai.cost.total_usd, for tracing and monitoring purposes.

Benefits of Standardized Cost Exposure

The standardization of cost exposure in LlmResponse offers numerous benefits:

• Improved Cost Management: Developers can track and manage LLM costs more accurately and efficiently.
• Enhanced Transparency: Clear cost breakdowns facilitate better understanding of expenses.
• Simplified Integration: Standardized cost metadata simplifies integration with cost monitoring and reporting tools.
• Increased Flexibility: Applications can switch between LLM providers more easily without significant code changes.
• Reduced Complexity: Developers no longer need to maintain custom price sheets or patch adapters to access cost information.

Conclusion: A Step Towards Efficient LLM Cost Management

Exposing per-invocation cost in LlmResponse is a significant step towards more efficient and transparent LLM cost management. By standardizing cost metadata, ADK can provide developers with the tools they need to accurately track and manage expenses across various LLM providers. This not only simplifies cost tracking but also enhances the flexibility and scalability of applications that leverage LLMs. As the use of LLMs continues to grow, the importance of effective cost management will only increase, making this standardization a crucial advancement in the field.

For further reading on best practices in AI development and cost management, consider exploring resources like the Google AI Blog. This will help you stay updated on the latest advancements and strategies in the AI landscape.