Customer Quota Overview
Model Backends
CheckingRegistered Local Models
Public model aliases routed securely to internal LLM engines (Ollama, vLLM, etc.)
Customer Accounts & API Keys
Manage tenants, allocate monthly token budgets, and issue hashed Bearer API keys.
| Customer Name | ID | Tier | Status | Token Budget | Used Tokens | API Keys | Actions | |
|---|---|---|---|---|---|---|---|---|
| Loading customer accounts... | ||||||||
User Login Accounts
Manage user login credentials, assign roles, and bind accounts to customer profiles.
| Role | Status | Attached Customer Profile | Token Quota | Created | Actions | |
|---|---|---|---|---|---|---|
| Loading users... | ||||||
My API Keys
Manage Bearer API keys for your applications. All inference usage is metered to your account.
| Key Name | Key Prefix | Status | Tokens Used | Expires | Last Used | Created | Actions |
|---|---|---|---|---|---|---|---|
| Loading your API keys... | |||||||
Request Logs
Inference audit: key prefix, model, status, latency, and tokens.
| Time | Customer | Key | Model | Status | Tokens | Latency | Error |
|---|---|---|---|---|---|---|---|
| Loading request logs... | |||||||
Usage Breakdown
Token consumption metered by day, model, and API key.
By Day
By Model
By API Key
API Test Console
Test authentication, model listing, usage, and chat completions.
Request Parameters
Response
Awaiting testClick "Send Request" to execute API call...
Developer Integration Guide
Connect your applications using standard OpenAI client libraries without code modifications.
🐍 Python (OpenAI Official SDK)
import os
from openai import OpenAI
# Initialize client pointing to your One-Router gateway
client = OpenAI(
base_url="https://api.one-router.ai.one-bit.net/v1",
api_key="sk-llm-your-api-key"
)
# 1. List Available Models
models = client.models.list()
print("Available Models:", [m.id for m in models.data])
# 2. Chat Completion
response = client.chat.completions.create(
model="qwen3-32b",
messages=[
{"role": "system", "content": "You are a secure local AI."},
{"role": "user", "content": "Explain zero trust architecture."}
],
temperature=0.7
)
print(response.choices[0].message.content)
🌐 cURL / Shell Request
# 1. Check Health
curl -s https://api.one-router.ai.one-bit.net/health
# 2. Fetch Models
curl -s -X GET "https://api.one-router.ai.one-bit.net/v1/models" \
-H "Authorization: Bearer sk-llm-your-key-here"
# 3. Chat Completion
curl -s -X POST "https://api.one-router.ai.one-bit.net/v1/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-llm-your-key-here" \
-d '{
"model": "qwen3-32b",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'
AI Chat Console
Interactive multi-turn AI playground powered by your local LLM engines (Qwen 3.8, Qwen 3.6, Gemma 4).