You’re overpaying for LLMs.
You just don’t know where.We find the exact LLM calls that burn cash.
And tell you how to stop them.
And tell you how to stop them.
No upfront payments required
| Timestamp | Provider / Model | App | Tokens | Cost | Speed | Finish |
|---|---|---|---|---|---|---|
| Feb 1, 08:39 PM | GPT-4 | Chatbot v2 | 1,847 → 312 | $87.40 | 14.2 tps | stop |
| Feb 1, 08:39 PM | Claude 3 Opus | Agent | 3,205 → 0 | $124.50 | 8.7 tps | stop |
| Feb 1, 08:39 PM | Gemma 3 4B (free) | Unknown | 427 → 105 | $0 | 10.3 tps | stop |
| Feb 1, 08:39 PM | GPT-4 | Chatbot v2 | 956 → 189 | $63.20 | 12.1 tps | stop |
| Feb 1, 08:38 PM | Claude 3 Opus | Summarizer | 5,102 → 743 | $198.30 | 6.4 tps | stop |
| Feb 1, 08:38 PM | Gemma 3 4B (free) | Unknown | 227 → 0 | $0 | 22.1 tps | stop |
| Feb 1, 08:38 PM | GPT-4 | Agent | 2,384 → 501 | $112.80 | 11.5 tps | stop |
| Feb 1, 08:38 PM | Claude 3 Opus | Chatbot v2 | 768 → 0 | $52.10 | 9.3 tps | stop |
This is how your LLM billing usually looks like.
Nothing seems wrong.
Everything looks normal.
But many of these requests add no value and quietly burn money.
LLM Cost makes them visible.
Your LLM costs don't come from usage.
They come from decisions.
Which model is used.
When requests are cached.
When the LLM is called at all.
You need a way to see which decisions are wrong.
What LLM Cost does
LLM Cost analyzes real LLM requests in production.
It looks at prompts, models, frequency, and behavior.
Then it shows which requests waste money and what you can do about them.
This is not reporting.
This is control.
Your App
LLM Cost
- Observe traffic
- Analyze in background
- Recommend fixes
OpenAI
Anthropic
GeminiAnalysis runs in the background. LLM requests are never delayed.
What LLM Cost gives you
From totals to specific requests
Stop looking at charts. See the exact LLM requests that cost money.
From 'something is wrong' to clear reasons
Understand why each request is expensive: wrong model, no cache, or pointless repetition.
From endless tuning to safe fixes
See which changes are safe and which ones actually matter.
From guessing to priority
Fix the few decisions that drive most of the bill.
LLM cost forensics in action
Built for
Teams running LLMs in production
Real users and real traffic
AI spend that already hurts
Engineers who need answers, not charts
Teams fixing cost regressions, not just tracking usage
Not for
Usage dashboards
Token counters
"Yesterday vs today" statistics
Tools that duplicate provider billing
Observability without accountability
Easy integration
No rewrites. No migrations. No surprises. Drop LLM Cost in front of your existing LLM client and start collecting data immediately. Your requests and responses pass through unchanged, with analytics running safely outside the request path.import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://proxy.llmcost.co/v1",
apiKey: process.env.OPENAI_API_KEY,
defaultHeaders: {
"X-LLMCost-Key": process.env.LLMCOST_TENANT_KEY,
},
});
const response = await client.chat.completions.create({
model: "gpt-4o-mini",
messages: [{ role: "user", content: "Hello!" }]
});Pricing plans
Pricing is based on how much LLM traffic we analyze —
not on dashboards or features.
Why workers: Forensic workers continuously analyze traffic in the background. More workers = faster detection of new waste patterns.
LLM Cost is not observability.
It is LLM cost forensics.
Every wasted LLM request
is lost revenue.