Tag every LLM call to the customer and feature behind it. See margin leaks in real time - not 28 days later when the bill arrives.
Free during beta - Read-only setup - No prompt capture
| Customer ID | Monthly Revenue | LLM Cost | Gross Margin | Status |
|---|---|---|---|---|
| cust_8a2f | $199/mo | $4.20 | 97.9% | Healthy |
| cust_3k9m | $99/mo | $6.80 | 93.1% | Healthy |
| cust_7p1n | $99/mo | $38.40 | 61.2% | Watch |
| cust_2r5t | $49/mo | $44.10 | 10.0% | Unprofitable |
| cust_9x8q | $49/mo | $51.30 | -4.7% | Losing Money |
The Problem
Total spend tells you what you burned. It doesn't tell you which customers burned it at a loss.
Your LLM bill tells you what happened 28 days ago. By the time a customer shows up as unprofitable, you've already served them at a loss for a full billing cycle. Every 30 days, the damage is already done.
avg discovery delay: 28 daysTwo customers on your $99/mo plan. One costs $4 in tokens. One costs $51. Your provider dashboard treats them the same - so you do too, until one quietly breaks your margin.
heavy users skew COGSWithout workflow attribution, a runaway agent looks exactly like normal usage - until the bill arrives and you're left reverse-engineering what happened.
alerts before invoicesThe missing layer
The number that matters isn't how much you spent on AI. It's whether the customers who drove that spend paid you enough to cover it.
Tokonomics sits between your LLM provider and your revenue. It connects spend to customer, feature, and workflow - so you're not reading a surprise invoice.
AI cost reckoning
$500M. One month. One client.
An enterprise client was reportedly billed $500 million in a single month for AI usage - no usage caps, no per-user visibility, no alerts. The invoice was the first signal anything was wrong.
It is a visibility problem. If your AI product cannot connect usage to customers, revenue, and workflows, every invoice is a delayed alarm.
UberAI coding budget reportedly burned out 4 months ahead of schedule
MicrosoftExternal Claude Code licenses pulled back as compute costs climbed
AmazonInternal leaderboard shut down after tokenmaxxing drove waste
Meta
DuolingoBoth teams tightening AI policies and adding usage caps - after the spend already happened
For most founders, the version is quieter: one customer slowly losing money every month until the invoice finally makes it obvious.
The pattern is always the same: usage runs ahead of visibility. Tokonomics flips that. Total spend first. Customer cost next. Real gross margin when revenue is connected.
How It Works
Tokonomics gives you value at every setup step, from zero-code provider visibility to customer-level gross margin.
total spend
model mix
spike alerts
Read-only key. Zero deploy. Paste your OpenAI, Anthropic, or Gemini key and you'll immediately see total spend, model mix, cost trends, and spike alerts. No code. No infrastructure change. Just visibility.
visible in 30 seconds
Add tokonomics tracking to every LLM call. Use the logged-in user as customer_id, infer the feature name from the route, and keep prompt content private.
Tell Tokonomics your stack - Cursor, Replit, Lovable, Claude Code, or hand-coded - and you get a ready-made snippet. Drop it in and every LLM call gets tagged to the customer and feature behind it. One line. Real attribution.
one line of tracking
Revenue minus AI cost, per customer, updated daily. That's the number you've been missing - and the one your investors will eventually ask about.
margin per customer, daily
Features
You're not running a DevOps team. You don't need another observability tool. You need to know which customers are costing you money - and what to do about it.
Every LLM call tagged to a customer, feature, and workflow. See your exact cost-to-serve - per customer, live. No aggregates. No guessing. Just the number.
gross.margin
COGS
Stripe revenue minus LLM COGS, per customer, updated daily. The number your investors will ask about - and the one that tells you who's actually making you money.
Alert before customer spend crosses $42 today.
Set a daily or monthly cost ceiling per customer or feature. Get the alert before it becomes a crisis - not after the invoice confirms it.
before
GPT-4o
$0.038/call
after
Haiku
save $38/mo
Think Haiku could replace GPT-4o for your simpler queries? Test it on real traffic first. Side-by-side quality comparison - so you know before you ship.
MRR lift
+4.1%
margin
91.2%
Wondering if usage-based pricing would fix your margin problem - or kill your conversions? Simulate the MRR impact before you touch your pricing page.
AI Margin Score
78
One number that tells you whether your AI unit economics are improving - and exactly what's dragging the score down this week.
2 customers crossed margin floor
1 workflow spiked 240%
3 model swaps ready for review
Every Monday in Slack or email: which customers crossed your margin floor, what shifted, and 1-2 actions worth taking. Short enough to read before standup.
Integrations
OpenAI - Anthropic - Google Gemini - LangChain - Cursor - Replit - Lovable
# Install: pip install tokonomics
from tokonomics import track, Tokonomics
toko = Tokonomics(api_key="tok_live_...")
@track(customer_id=request.user_id, feature="chat")
async def generate_response(message: str) -> str:
response = await openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": message}]
)
return response.choices[0].message.contentFounder pains
Hidden heavy users
cust_9x8q
"One customer on my $49/mo plan costs me $51 in tokens every month. I had no idea until I checked manually. I need to see this without having to dig."
-4.7% marginPricing uncertainty
pricing.sim
"I think I need to move to usage-based pricing. But I don't know if that means $400 more in MRR or losing 20% of my signups."
+$420 MRRModel waste
route.waste
"I'm 90% sure my app is calling GPT-4o on tasks that Haiku could handle. I just can't prove it without spending a full day in logs."
$38/mo leakVibe-coded stack
vibe.build
"I built this fast. I genuinely don't know what model my app calls, how often, or at what cost. I need the map before I touch anything."
unknown AI stackROI Calculator
Move the sliders. See how quickly margin clarity pays for itself.
Move the inputs to see how quickly margin visibility pays back.
Estimated monthly benefit
Model routing$1,250 /mo
Repricing lift$396 /mo
Incident prevention$800 /mo
Total benefit$2,446 /mo
Growth plan$199 /mo
Net gain$2,247 /mo
Directional estimate - real results depend on your stack, pricing model, and usage patterns. But the math tends to hold.
Pricing
You don't need to integrate everything on day one. Start with your API key, get real spend data, and upgrade when the next layer makes sense.
See your spend. Immediately.
See what each customer actually costs you.
See real margin. Then fix what's breaking it.
Margin ops across the whole team.
Free during beta for all early access users. No card required to start.
Start with total AI spend visibility - no card, no code, no deploy. Add customer attribution and Stripe margin when you're ready.