TokenDam: A Client-Side Tool for Auditing and Reducing LLM Token Waste

Research context and background

TokenDam is a client-side utility designed to help developers and teams audit measure and reduce token waste in their Large Language Model integrations. Unlike traditional proxies or gateways that route production traffic TokenDam operates entirely in the browser. This ensures that no prompts or data are uploaded to external servers. It provides precise measurements of token usage and costs enabling teams to identify inefficiencies and optimize their AI spending.

Benefits

TokenDam offers several key advantages for engineering teams. First it provides zero-setup auditing. Users can generate a report in approximately two seconds by simply pasting a trace or request payload. This makes it ideal for quick audits and identifying waste at the source without disrupting live operations. Second it delivers precise cost measurement. Unlike asking an LLM to guess where tokens are being wasted TokenDam provides exact measurements. It calculates token counts using a fair 200k tokenizer and multiplies them by current pricing to generate a trustworthy dollar figure. This consistency allows for reliable tracking and comparison over time. Third it supports CI/CD integration. TokenDam offers a tokendam diff regression gate that can be integrated into Continuous Integration pipelines. Similar to ESLint it can fail a build if token usage exceeds a defined budget ensuring that regressions in efficiency are caught early.

Use Cases

TokenDam is best used for auditing and fixing waste at the source. Teams can capture traces from agent runs which include caching history and duplicate-document waste. These issues often only become apparent across multiple calls. Developers can use the capture helper in any SDK to automatically write a trace to a file. They can also log request arguments directly when using raw OpenAI or Anthropic SDKs. For those using the Vercel AI SDK they can wrap the fetch call to log the body. Users working with LangChain or LangSmith can read inputs from a specific run ID. Once the trace data is captured users can forward this data to the TokenDam service. The service processes the input and generates a report including a report for your boss in PDF format to help stakeholders understand the financial impact of token usage.

Pricing

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Vibes

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Additional Information

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