Local LLM Gateway Control Center

Enterprise-grade OpenAI compatible gateway for private on-premise LLMs with token metering & quota enforcement.

API Base URL https://api.one-router.ai.one-bit.net/v1
Active Models 🤖
1
Local backends connected
Customers & Projects 👥
1
Active tenant accounts
Total Tokens Used ⚡
0
Input & Output metered
Requests Today 🛡️
0
Failed: 0

Customer Quota Overview

Loading quota status...

Model Backends

Checking
Checking model health...

Registered Local Models

Public model aliases routed securely to internal LLM engines (Ollama, vLLM, etc.)

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Customer Accounts & API Keys

Manage tenants, allocate monthly token budgets, and issue hashed Bearer API keys.

Customer Name ID Tier Email Status Token Budget Used Tokens API Keys Actions
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User Login Accounts

Manage user login credentials, assign roles, and bind accounts to customer profiles.

Email Role Status Attached Customer Profile Token Quota Created Actions
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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
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Request Logs

Inference audit: key prefix, model, status, latency, and tokens.

Time Customer Key Model Status Tokens Latency Error
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Usage Breakdown

Token consumption metered by day, model, and API key.

By Day

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By Model

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By API Key

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API Test Console

Test authentication, model listing, usage, and chat completions.

Request Parameters

Paste your customer API key or click "Auto-fill My Key".

Response

Awaiting test
Click "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).

🤖
One-Router AI Assistant
Halo! Saya siap membantu. Pilih model di atas (Qwen 3.8, Qwen 3.6, atau Gemma 4), lalu ketik pesan atau klik tombol pertanyaan cepat di bawah untuk mulai menguji!