Henry Alfano pro California 59M AGO

I built a workflow to reconstruct auditable, per-client billing for Claude usage after my Anthropic bill tripled in one month and I discovered there was no reliable way to see why. I run a solo consulting practice on a Max plan and pass AI costs through to clients. In September, my extra usage reached $1,805.76 in nineteen days. The usage page attributed 100 percent of one week to "Cowork" without session detail. Auto-reload was enabled by default with no spending limit, and the only usage I could independently verify—my local Claude Code logs—accounted for about $245. The support bot twice described analytics features that did not exist, including a per-day view it later retracted. When I finally reached a human, I got the truth in writing: Max subscribers have no session-level or per-day reporting, exports are Enterprise-only, and Cowork has been cloud-only on consumer plans since August. The Enterprise path to a readable meter starts at twenty seats. I am one person, so I built my own meter. The irony is that I used Claude to design and code it. Claude Code sessions write logs on my own machine. I price them at list rates with the open-source `ccusage` tool and attribute each session to a client by repo folder. That creates the measured pool: real receipts with no allocation. Other CLIs in the same logs, such as OpenAI Codex, are excluded automatically. Chat and Cowork run in Anthropic’s cloud and leave nothing on my disk, so the remaining overage becomes a cloud pool. I split that pool using evidence my workflow already produces: every Cowork document batch leaves a directive file, snapshots, and a report on disk. A Python script counts executed batches per project and measures the number of bytes each one rewrote. The script splits the cloud pool independently by batch count and byte volume, bills the midpoint, and shows both derivations so each client can see the bracket. Every column ties back to the dashboard total to the penny. A second script pulls per-account AWS charges from Cost Explorer because each client has a separate AWS account. Shell aliases make each report a one-command operation. A Claude skill and a monthly scheduled task turn the outputs into a draft invoice that I approve before anything is sent. The result is that a vendor hands me one unexplained number each month, while I hand my clients an evidence file. Month-end billing takes five minutes. It should not have been necessary to build this, but until consumer plans get real usage analytics, this is how I see in the dark. Step-by-step: 1. I collect local Claude Code session logs and use the open-source `ccusage` tool to price them at list rates. 2. I attribute each Claude Code session to a client by repo folder and automatically exclude other CLIs, such as OpenAI Codex, from the measured pool. 3. I treat the remaining overage from cloud-based Chat and Cowork usage as a separate cloud pool because those services leave no usage logs on my disk. 4. I use Cowork’s directive files, snapshots, and reports to identify document batches, then use a Python script to count executed batches per project and measure the bytes each batch rewrote. 5. I split the cloud pool independently by batch count and byte volume, bill the midpoint, and show both derivations as a client-facing bracket. 6. I verify that every column ties back to the dashboard total to the penny. 7. I use a second script to pull each client’s per-account AWS charges from Cost Explorer. 8. I run the reports through shell aliases, then use a Claude skill and a monthly scheduled task to create a draft invoice for my approval before sending it.