REAL-TIME LLM COST INTELLIGENCE

Your AI spike happened three days ago. Your dashboard just told you.

Every dollar the instant a request completes, broken down by feature, model and customer. Text, image and voice priced component by component. One async call, no proxy, nothing added to your request path.

No card · the free plan prices exactly like the paid one

WHAT DOES YOUR MODEL COST?live rates

3 min in × 600 tok/min = 1,800 audio input tokens

2 min out = 1,200 audio output tokens

plus ~60 in / ~120 out text tokens per turn

componenttokens / monthrate / 1Mcost
text in120,000$0.3$0.04
audio in3,600,000$1$3.60
text out240,000$2.5$0.60
audio out2,400,000—not published—not published
Total$4.24

This model publishes no audio out rate, so this estimate is incomplete.

$4.242,000 requests / month
A tool with one input rate and one output rate shows $7.72 — -82% lower, because it bills audio at the text rate.

These are one expensive and one cheap model per category. 420 models are priced in full. See every model's price → Open the full calculator →

420models priced, synced daily
<1 msadded to your request
1line of code to integrate
0prompts stored, ever

THE PROBLEM

An invoice tells you what. It never tells you which.

Your provider bills you after the fact, in one number, with no idea what your product does.

Surprise bills at month end

Your provider bills you after the fact. By the time the invoice lands, the spike is three weeks old and the deploy that caused it is buried.

Which deploy did this?

No feature-level visibility

One monthly total. No breakdown. The chat assistant and the batch summariser look identical from the outside, and one of them is 80% of your bill.

Which feature costs this?

Model switching in the dark

Same provider, 20× price gap between tiers. Moving a feature to a cheaper model might save hundreds or might quietly ruin the output quality.

Can I afford to switch?

MECHANICS

How a cost actually gets calculated

No proxy, no prompts stored, nothing estimated that could be measured. Here is the whole path, because a number you can't check is a number you shouldn't trust.

01 — YOU SEND THE COUNTS

One call, after your request has already returned

Your provider hands you a usage object with every response. You pass those counts to track() with a feature name. That's the integration.

It is fire-and-forget and sits outside your request path, so it cannot slow down or break what your users are waiting for. Prompts and completions never leave your infrastructure — we take counts, not content.

02 — WE PRICE EACH COMPONENT

Seven components, seven rates, never blended

A modern request is not two numbers. It is text in, cached reads, cache writes, audio in, reasoning, text out and audio out, and each one bills at its own published rate.

billableInput  = prompt_tokens − cached − audio_in
billableOutput = completion_tokens − reasoning − audio_out

cost = billableInput  × input_rate
     + billableOutput × output_rate
     + cached         × cache_read_rate
     + cache_writes   × cache_write_rate
     + audio_in       × audio_in_rate
     + audio_out      × audio_out_rate
     + reasoning      × reasoning_rate

03 — CACHING COUNTS PROPERLY

Cached tokens bill at the cache rate, not the input rate

If you cache a prompt prefix, those tokens cost a fraction of full price — 90% less on one flagship model, 50% less on another. The discount is never a constant, so we read each model's own cache rate instead of assuming one.

Providers also disagree about whether cached tokens sit inside the prompt count or beside it. Get that backwards and you charge them twice. We resolve it per request, checking the model before the provider, so a model served through a reseller still resolves correctly.

Cache writes cost more than ordinary input. We show that too, because caching can make a bill worse and nobody warns you about that.

04 — YOU SEE IT IN UNDER A SECOND

And we tell you when we had to approximate

Rates come from each provider's published schedule and are re-synced every day, so a price cut appears without anyone hand-editing anything. Cost is computed on our side, at ingest — we never accept a cost figure from your client.

When a provider publishes no rate for a component, we fall back to the standard multiplier and mark the row as derived. When we can't tell which convention a response follows, we price what we're sure of and flag the rest. A number you can see is uncertain beats a number that's quietly wrong.

The free plan runs this same engine on your real traffic. Start tracking free →

WHAT YOU GET

Answers, not just numbers

Cost tracking tells you what you spent. These tell you what to do about it.

MODEL INTELLIGENCE

The only tool that tells you not to switch

Every cheaper model comes with a quality cost. We check each switch against independent benchmarks for the kind of work that feature actually does, and say so when the saving isn't worth it.

Keepcode-review · claude-sonnet-5

Available saving$43.69 per month

Coding index73 → 52 (−21)

Our verdictdeclined

CACHE INTELLIGENCE

Know the hour your caching broke

Hit rate per feature, what caching saved you, and what a collapsed prefix is costing. It moves the moment someone edits a prompt — the invoice would tell you in three weeks.

Alertdocs-search · today 14:20

Hit rate85% → 4%

Cost of the drop+$8.40 per day

FORECAST

This month's bill, and next month's

A projection from your actual daily run rate, with the day you'll cross your budget. Not an extrapolation from a single week.

This month$824.76

Projected close$1,012

$600 budgetpassed on day 22

Causedocs-search, 94%

UNIT ECONOMICS

Cost per customer, against what they pay

The question no observability tool answers: is this customer profitable? Tag requests with a customer ID and see margin per account, not just spend per model.

acme-corp · Pro $29$61.20 AI cost

northwind · Pro $29$3.10 AI cost

Margin spread−25% to +94%

MODEL PRICES

Every model's real price, synced 1 hour ago

Input, output, cached reads, cache writes and audio, per million tokens. Public, dated, and the same sheet that prices your dashboard.

420 models · 12 providerslast sync 13 Oct 2026 14:29 UTC · 1 hour ago
MODELPROVIDERINPUTOUTPUTCACHEDCACHE WRITEAUDIO INMODALITIES
claude-sonnet-5anthropic$2$10$0.2$2.5—textimagepdf
gpt-realtime-2.1openai$4$24$0.4—$32textaudioimage
gemini-3-pro-imagegoogle$2$120———textimage
gpt-4o-miniopenai$0.15$0.6$0.07——textimagepdf
gemini-3.1-flash-litegoogle$0.25$1.5$0.03—$0.5textimagevideoaudiopdf

A dash means the provider publishes no separate rate for that component. We never invent one — the row in your dashboard says so instead.

See all 420 model prices →

HONEST COMPARISON

Where we fit, and where we don't

If you need full request tracing, Langfuse and LangSmith do that and we don't. If you need to know which feature and which customer costs what, that's this.

LLMtrackHeliconeLangfuseLangSmith
Cost per feature✓partialpartialpartial
Cost per customer, with margin✓✗✗✗
Audio priced separately from text✓✗✗✗
Cache hit rate and savings✓✗partial✗
Benchmark-backed switch advice✓✗✗✗
Proxy requirednopartialnono
Prompts storedneveryesyesyes
Full request tracing✗✓✓✓
Setup time<2 minutes15 min30 min60+ min

Competitor figures checked 2 October 2026 against each vendor's public pricing page.

PRICING

Simple pricing. No surprises.

Start free. Pay only when your usage grows. No annual lock-in. No sales calls.

Free

The full experience, free

$0forever
  • 3,000 events / month
  • Full analytics dashboard
  • Unlimited integrations
  • Official Node & Python SDKs
  • Model savings recommendations
  • Feature & per-customer cost breakdown
  • Cost calculator with your real usage
  • Spend forecast
  • CSV export
  • 30-day data retention
Get started free
Coming soon

Business

Built in public. Prices synced this morning.

Last price sync

13 Oct 2026 14:29 UTC1 hour ago

eu.anthropic.claude-sonnet-5-5cache_read −50%

us.anthropic.claude-sonnet-5-5cache_read −50%

global.anthropic.claude-sonnet-5-5cache_read −50%

anthropic.claude-sonnet-5-5cache_read −50%

420 models, 12 providers

Every estimate on this page, and every cost in your dashboard, uses this sheet.

See every model's price

Changelog

Audio tokens priced separately from text on multimodal models

Cached and reasoning tokens no longer double-charged on OpenAI-style responses

Python SDK adds track_sync() for awaited delivery

Cost per user, with margin against plan price

Full changelog

Why we built LLMtrack

It started with a number that didn't make sense: a bill four times what it should have been, and nothing anywhere that could say which feature caused it.

So the brief was narrow. Don't touch the request. Don't store the prompt. Just answer the question an invoice never does — which part of the product costs this much, and for which customer.

support@llm-track.com

QUESTIONS

Before you integrate anything

Do you see my prompts?

No. The SDK sends token counts, a model name and a feature label. Prompts and completions never leave your infrastructure, and the SDK has no field for them.

Will this slow down my app?

No. The call is fire-and-forget and happens after your provider has already responded. There is no proxy and nothing sits in your request path.

How do you price image and audio models?

In tokens, like the providers do. An image becomes output tokens, a second of speech becomes audio tokens, and audio bills at its own rate — up to eight times the text rate on the same model. Each component is priced separately against that model's published schedule.

What happens if you don't know a model?

The event records with an unpriced status and a visible badge. It is never recorded as costing $0, because a silent zero is indistinguishable from a request that genuinely cost nothing.

How current are the prices?

Re-synced every day from each provider's published schedule, with the sync timestamp shown publicly on this page and in your dashboard.

Can I just log usage to my own database?

Yes, and for one model and one feature you probably should. The part that becomes work is the price table: rates change, models are deprecated, and cache and audio rates are often published weeks after a model launches. A stale rate still produces a confident number.

Your first cost breakdown in under 2 minutes

Install the SDK, add one line after your existing call, and watch the first request land. Free forever, no card, no proxy.