# Show HN: Marginal – See which customers and features drive your AI API costs

> Source: <https://marginalhq.com/>
> Published: 2026-08-19 13:23:17+00:00

TypeScript SDK · Python SDK · HTTP API

# See where your AI spend goes.

Marginal tracks the cost of every LLM call and slices it by the fields you care about — customer, feature, model.

npm install marginal-sdk · pip install marginal-sdk

The dashboard

## From “the bill doubled” to a named cause

Slice spend by what matters in your product, see every dimension charted automatically, and check exactly what your integration sent.

### Spot the spike without building a chart

Insights charts spend for every dimension automatically — each model, provider, and registered field gets its own chart. No setup, no query builder.

### Know exactly what landed

Every API request is logged with its outcome — accepted counts, rejected events and why, stripped keys, unpriced models. Debug your integration without guessing.

### Never a silent $0

Unknown model? The event still lands, flagged as unpriced — visible in the dashboard and the API response, never averaged away. Set a custom price and it applies at ingest.

How it works

## One call per LLM request. That's the whole integration.

- 1
### Pick your connection

`npm install marginal-sdk`

,`pip install marginal-sdk`

, or no install at all — POST JSON to the HTTP API from any language. The SDKs are zero-dependency, buffered, fire-and-forget: track() never throws and never blocks your request path. - 2
### Track each LLM call

Name the provider, paste the response's

`model`

and`usage`

as-is — Marginal detects the usage shape and computes the cost server-side against a daily-synced model price catalog. - 3
### Slice your spend

Group and filter by customer, feature, model — any field you register. Save the views you keep coming back to.

``` js
import { Marginal } from "marginal-sdk";

const marginal = new Marginal({ apiKey: process.env.MARGINAL_API_KEY });

const response = await openai.chat.completions.create({ /* … */ });

marginal.track({
  provider: "openai",
  model: response.model,
  usage: response.usage,
  fields: { customer: "acme-corp", feature: "support-bot" },
});
python
import os

from marginal import Marginal

marginal = Marginal(api_key=os.environ["MARGINAL_API_KEY"])

response = client.chat.completions.create(...)

marginal.track(
    provider="openai",
    model=response.model,
    usage=response.usage.model_dump(),
    fields={"customer": "acme-corp", "feature": "support-bot"},
)
curl -X POST https://api.marginalhq.com/v1/events \
  -H "Authorization: Bearer $MARGINAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "events": [
      { "provider": "openai",
        "model": "gpt-4o-2024-08-06",
        "usage": { "prompt_tokens": 2006, "completion_tokens": 300 },
        "fields": { "customer": "acme-corp", "feature": "support-bot" } }
    ]
  }'
```

The cost lands computed, priced at that day's catalog rates — no token math in your codebase.

Cost observability

## Built to answer “why was the bill that high?”

### Slice by any field

Register your vocabulary — customer, feature, anything you slice by — then group or filter spend by it. Unregistered keys are stripped and reported back, so dashboards stay clean.

### LLM-aware pricing

Send provider, model, and the response's usage object; cost is computed server-side from a daily-synced price catalog, with per-project overrides. Prices are frozen at ingest.

### Saved views

Any Explorer state — range, filters, group-by — saves as a named view. The questions you ask every week are one click away.

### SDKs that stay out of the way

TypeScript and Python, zero dependencies. Events buffer locally and flush in batches in the background; network failures retry and then warn — your app never notices. Prefer no SDK? The HTTP API is a single JSON POST.

## Let your coding agent do the integration

Paste [marginalhq.com/llms.txt](/llms.txt) into Claude Code, Cursor, or any coding assistant and it has everything it needs to wire up Marginal — or follow the per-provider recipes yourself.

[Integration recipes](/docs/integrations)

## From zero to a live spend dashboard in five minutes.

Create a project, register your fields, drop in one track() call. Your next deploy starts answering questions.