How I Track My AI Coding Costs Per Project (and Bill Them to Clients)

zhx842htt — Sat, 03 Oct 2026 10:37:04 +0000

If you deliver software with Claude Code (or Cursor, or raw API keys), you probably know roughly what you spend on AI each month. What you almost certainly can't answer is:

"Which project — or which client — consumed that spend?"

That question matters if any of your work is billed. Hours and licenses show up on invoices; AI costs currently hide inside a terminal. This post shows where the data actually lives, how to turn it into per-project dollars, and the tooling I built to make it one command.

The data was on your machine all along

Claude Code writes every session to local JSONL files:

~/.claude/projects/<project-slug>/<session-id>.jsonl

Each line is an event. Assistant messages carry the interesting part — a message.usage object with token counts, the model name, plus the session's cwd (which is how you know which project the tokens belong to):

{
  "type": "assistant",
  "timestamp": "2026-09-20T10:00:00.000Z",
  "sessionId": "session-a",
  "cwd": "/home/dev/clientA-api",
  "message": {
    "model": "claude-sonnet-4-20250514",
    "usage": {
      "input_tokens": 1200,
      "output_tokens": 800,
      "cache_creation_input_tokens": 5000,
      "cache_read_input_tokens": 20000
    }
  }
}

Group by cwd, sum tokens, multiply by prices — done? Almost. Three gotchas first.

Gotcha 1: naive token × price is wrong

Most of the tokens in an agentic coding session never hit list input price. The big three buckets:

Bucket	Typical price vs input	Why it dominates
`input_tokens`	1×	the actual new context
`cache_creation_input_tokens`	~1.25×	writing context to cache
`cache_read_input_tokens`	~0.1×	re-reading cached context — the majority in long sessions

Ignore the cache split and your estimate can be off by several times. The formula per message:

billable = input×1 + cache_write×1.25 + cache_read×0.1   (× input price)
         + output×output_price

Gotcha 2: duplicate lines

Streaming writes can emit the same assistant message more than once. Dedupe on message.id (unique per message) — or your costs inflate silently. A fingerprint of timestamp + sessionId + token counts works for older formats without ids.

Gotcha 3: prices drift

Model pricing changes often enough that hardcoding numbers in a script you'll forget about is a trap. Keep a price table in a config file (~/.devspend/prices.json overrides the built-ins) and treat every output as an estimate until you calibrate it.

Putting it together: one command

I packed the above into a small zero-dependency CLI, devspend (MIT, open source — I'm the author, to be clear):

npx devspend projects

项目路径                          花费        输入tok       输出tok
/clients/acme-rewrite           $142.80      31.2M         1.8M
/clients/globex-mvp              $96.15      19.7M         1.1M
/side-projects/devspend          $12.40       2.1M         0.3M
─────────────────────────────────────────────────────────────
合计                             $251.35

It reads only local files, sends nothing anywhere, and also does day/model breakdowns (devspend report, devspend models). The full source is short enough to audit in one sitting — which is the point; billing numbers should be verifiable.

GitHub: zhx842htt/devspend
Waitlist for the billing-grade features: devspend.netlify.app

The part that actually pays: billing

Once spend is attributed per project, two defensible billing models emerge:

Rate-card billing (API users): your token costs at list prices become a line item, like billable hours. Estimate-based, transparent, easy to defend.
Cost-pool allocation (subscription users): your Claude Max / Cursor subscription is a fixed pool; allocate it across clients proportionally to usage. Arguably more honest than rate-card, since it matches your real cash outflow.

Either way the win is the same: a cost that used to silently eat your margin becomes visible, discussable, and recoverable. The first freelancer conversation I'd love to hear more about is simply: do your clients accept AI as a billable line today, or is everyone still rolling it into the rate? Genuine question — comments open.

Roadmap

API-key usage ingest (OpenRouter/OpenAI/Anthropic) and subscription cost pools are next; client entities and invoice-grade exports after that. The CLI stays free and open — the billing workflow is what becomes Pro.

If you've built your own tracking scripts, I'd genuinely like to compare notes.

DEV Community: zhx842htt