Today's Spend
$0.00
This Week
$0.00
This Month
$0.00
API Calls
0
Tokens
0
Avg Latency
0ms
Error Rate
0%
Cache Hit
0%
Daily Costs
30 daysToday's Hourly Spend
LiveModel Breakdown
Provider Distribution
Cost Forecast
StableProjected Monthly$0.00
Average Daily$0.00
Days Remaining0
Spent So Far$0.00
| Time | Provider | Model | Input | Output | Cost | Latency | Status | Cache |
|---|
Calculate Cost
Estimated Cost
$0.0000total cost
Per Call$0.0000
Input Cost$0.0000
Output Cost$0.0000
Input $/1M tokens$0.00
Output $/1M tokens$0.00
| Model | Provider | Avg Cost/Call | Avg Latency | Total Calls | Total Tokens | Success Rate |
|---|
Spending Limits
$
$
Notifications
How Alerts Work
Daily Limit
Triggers when daily spend reaches the limit. Resets at midnight.
Monthly Limit
Triggers when monthly spend reaches the limit. Resets on the 1st.
80% Warning
Warning alert when spend reaches 80% of your set limit.
Cost Spike
Auto-alerts when a single call costs 3x more than the model average.
AI-powered suggestions to reduce your LLM spending
Current pricing per 1M tokens for all supported models
Start free and upgrade when you grow. Cancel anytime.
Integrate LLM Cost Tracker into your application
1. Register & Get Token
POST /api/auth/register
curl -X POST http://localhost:8000/api/auth/register \
-H "Content-Type: application/json" \
-d '{"email": "you@company.com", "password": "secret123", "full_name": "Your Name"}'
2. Login
POST /api/auth/login
curl -X POST http://localhost:8000/api/auth/login \ -d "username=you@company.com&password=secret123"
3. Track API Calls
POST /api/track
curl -X POST http://localhost:8000/api/track \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"provider": "openai",
"model": "gpt-4o",
"input_tokens": 150,
"output_tokens": 50,
"latency_ms": 1200,
"status": "success",
"project": "my-app"
}'
Python SDK Example
Python
import httpx
class LLMTracker:
def __init__(self, base_url, token):
self.base_url = base_url
self.headers = {"Authorization": f"Bearer {token}"}
def track(self, provider, model, input_tokens,
output_tokens, latency_ms, status="success"):
return httpx.post(
f"{self.base_url}/api/track",
json={
"provider": provider,
"model": model,
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"latency_ms": latency_ms,
"status": status,
},
headers=self.headers,
)
# Usage
tracker = LLMTracker("http://localhost:8000", "your-token")
tracker.track("openai", "gpt-4o", 150, 50, 1200)
All Endpoints
GET/api/dashboardDashboard summary
GET/api/logsPaginated cost logs
GET/api/forecastCost forecast
GET/api/comparisonModel comparison
POST/api/trackLog an API call
POST/api/budgetSet budget limits
GET/api/budgetGet budget
GET/api/alertsGet alerts
GET/api/exportExport CSV
Profile
Quick Start Guide
Step 1: Create Account
Register with your email to get started
Step 2: Get API Token
Login and copy your bearer token
Step 3: Track Calls
Send POST requests to /api/track after each LLM call
Step 4: Monitor Dashboard
Watch your spending in real-time on the overview page