Find where your LLM API bill leaks—without uploading your logs

Description and use

linklbee‑scan is a local Python tool that analyzes LLM usage logs and produces an HTML report on costs, highlighting repeated requests, low cache hit rates, and truncated responses, enabling cost optimization without uploading data to third‑party services.

Problem

Determine which parts of an LLM API bill are driving costs and identify opportunities for savings without exposing sensitive logs.

Outcome

linklbee‑scan produces an HTML report that identifies cost drivers such as repeated calls, low cache hit rates, and truncated responses, allowing teams to target specific experiments for savings.

Workflow

• Install linklbee‑scan via pip
• Run the built‑in demo to view report format
• Provide your own log file or folder to linklbee‑scan
• Optionally use --peek to verify field mapping
• Review the generated report for cost patterns and findings
• Turn findings into experiments (e.g., reuse identical calls, adjust prompt length)
• Compare cost, quality, and latency before and after changes

Evidence

We built Linklbee Scan, an open‑source Python tool that reads existing usage logs on your machine and generates an HTML report to help answer those questions.

Source

No API key, no model calls, and no log uploads. It uses the Python standard library and runs on Python 3.9+.

Source

The scanner reads JSONL, JSON arrays, CSV/TSV, .log, and .gz files. It includes mappings for common provider responses and exports from tools such as Langfuse, LangSmith, Helicone, and LiteLLM.

Source