Show HN: FiscaLLM:open-source per-user AI cost for subscription apps

See the AI cost of every single user. FiscaLLM adds up what each user costs you in AI and sets it next to what they pay you, so you can see who is profitable and who is losing you money.

Open source (MIT) and free. Use the hosted service at fiscallm.com (free under fair use, best effort), or run your own.

Live demo (no sign-up) · Docs · How it works · What it covers · Accuracy · Roadmap (board) · Changelog · Self-hosting · Contributing

Sample data from the demo, not a real customer.

Create an app atfiscallm.com (or your own copy) and copy the ingest key. 2. Add one line after each AI call (it reads only the model name and token counts, never prompts or answers):

import { createMeter } from "fiscallm"; // npm install fiscallm   (Python: pip install fiscallm)
const meter = createMeter({ apiKey: process.env.FISCALLM_KEY, waitUntil });

const res = await openai.chat.completions.create({ model, messages });
meter.trackOpenAI(res, { userId, feature: "chat" });

Connect revenue : paste a RevenueCat or Stripe webhook URL, or send payments yourself with the revenue API.

No AI key handy? FISCALLM_KEY=fsk_... node examples/send-sample-data.mjs fills a test app with sample users so you can see it working.

AI apps charge a flat price but pay a variable cost. One user costs $0.20 a month in AI, another costs $25. Your AI provider's bill shows tokens, your subscription tool shows payments, and neither shows the user.

import { createMeter } from "fiscallm";

const meter = createMeter({ apiKey: process.env.FISCALLM_KEY, waitUntil });

const res = await openai.chat.completions.create({ model, messages });
meter.trackOpenAI(res, { userId, feature: "chat" });
python
from fiscallm import Meter

meter = Meter(api_key=os.environ["FISCALLM_KEY"])
res = client.chat.completions.create(model=model, messages=messages)
meter.track_openai(res, user_id=user_id, feature="chat")

• The SDK reads only the model name and token counts from the response. Never prompts or answers.
• Events are batched and sent in the background. Tracking never throws and never blocks your response.
• Each event has a client-generated UUID, so retries cannot double count.
• Revenue comes from a RevenueCat webhook, or from your own code with trackRevenue /track_revenue .
• Both are joined on the user ID. RevenueCat aliases are resolved, so anonymous and logged-in users merge.
• Budgets and alerts : email, Slack or signed webhooks when a user goes over budget, when a paying user loses money, or when one user burns a huge number of tokens in 5 minutes (a runaway loop).
• Spending caps : mark a per-user budget as Block and callmeter.check(userId) before an AI call (TypeScript, Python). It says no once the user reaches the amount, andfails open if FiscaLLM is slow or down.

Cost is priced on arrival: exact costUsd if given, otherwise per-token (input, output, cache read and cache write priced separately) or per-unit (images, audio, video) from a price table. Unknown models are flagged as unpriced, never counted as zero.

Net revenue is price × (1 − tax − store commission).

Stack: TypeScript, Next.js, Hono, Postgres (Supabase) with Drizzle, hosted on Vercel.

• It needs a backend between your app and the AI provider. Apps that call a provider straight from the phone are not covered yet.

• Revenue comes from RevenueCat, Stripe (beta, USD only) or the revenue API. Direct App Store and Google Play connections are not built yet.

• No AI key needed: FISCALLM_KEY=fsk_... node examples/send-sample-data.mjs fills the dashboard with sample users (one heavy user who loses money).

• Copy-paste starting points for Next.js andFastAPI , seeexamples .

• Run your own copy: SELF-HOSTING.md ).

pnpm install
pnpm dev:web   # http://localhost:3000
pnpm dev:api   # http://localhost:8787/api/health
pnpm test

Copy .env.example to apps/web/.env.local and packages/shared/.env and fill in the values.

pnpm db:generate
pnpm db:migrate

Issues and pull requests are welcome. Read CONTRIBUTING.md first (it includes a short contributor agreement). The plan, including what we only build if there is demand, is in ROADMAP.md, with a public board. Vote with a 👍 on roadmap issues or share ideas in Discussions.

Where help is most wanted: prices for more models and providers (data only, no code knowledge needed), connectors for other billing systems (Adapty, Superwall), examples for AI frameworks, and a comparison of computed costs with a real provider invoice. See the good first issues.

Built and maintained by Mayur as an independent, open-source side project. Questions, bugs and ideas: open an issue or email theaicommit@gmail.com.

Thank you to everyone who helps. Contribute: a good place to start is an issue labelled good first issue.

MIT. See LICENSE.