LLMPanel — Deploy LLMs on any GPU, anywhere

Before

Scattered tooling

• A different dashboard for every GPU cloud
• Hand-written docker run commands nobody remembers
• One API key shared by every app and teammate
• nvidia-smi in a tmux pane is the monitoring
• No idea what a model actually costs per hour

With LLMPanel

One control plane for inference

• Docker hosts, RunPod and Vast.ai in one fleet view
• Pick a model, pick a GPU, deploy — vLLM or Ollama
• Scoped keys with rpm / tpm / daily caps and revocation
• Per-GPU utilisation, VRAM and temperature, live
• Cost per hour and uptime on every deployment

Gateway

One endpoint, scoped keys, no surprises

Every deployment sits behind one OpenAI-compatible base URL. Hand each app or environment its own key with rpm, tpm and daily caps — and revoke it in one click when it leaks.

Gateway · API keys

NameKeyrpmUsage today

web-app · prodactivelp_live_8f2a…60071%

mobile · prodactivelp_live_c91d…30042%

evals · stagingactivelp_test_17b0…608%

limits · rpm · tpm · daily cap · expiry — per key

Metrics

See what every GPU is doing, right now

Utilisation, VRAM, temperature and power draw for every card across Docker hosts and cloud providers, plus container CPU and RAM — live and historical, no exporters to wire up.

Metrics · fleet

Logs

Drill into any request

Status, latency, prompt and completion token split, and which key made the call. Filter by status, model, key or time range and open the full payload inline.

idmodel · keystatusmstokens

Agents & RAG

Ground a model in your own docs

Attach an agent to any deployment with its own system prompt and knowledge base. Upload files, get chunking and embeddings for free, and test it in the built-in chat before wiring it to Telegram or Discord.

Agents · docs-assistantllama-3.3-70b · rag on

Ship any Hugging Face model on vLLM or Ollama to a Docker host or a cloud GPU in one flow.

One OpenAI-compatible base URL in front of every deployment. Point your SDK at it and go.

Per-GPU metrics, request logs and usage analytics — live and historical, no exporters to wire up.

Add a second host or burst to RunPod when demand spikes; the gateway keeps the same URL.

Scoped API keys with rpm, tpm and daily caps, expiry, per-key usage and one-click revocation.

Snapshot panel data and config on a schedule, and restore from any point when you need to.

01Step 01

Install the panel

One command on any Linux box with Docker. It brings up the API, the dashboard and the gateway, and prints the login URL.

curl -fsSL https://raw.githubusercontent.com/zf0x00/openpanel-oss/master/install.sh | bash

02Step 02

Connect your GPUs

Add a local Docker host, or paste a RunPod / Vast.ai API key. Every card shows up in the fleet view with live metrics.

03Step 03

Deploy and call it

Pick a model, pick a GPU, deploy. Point any OpenAI SDK at the gateway URL with a scoped key — nothing else changes.

from openai import OpenAI

client = OpenAI(
    base_url="https://gw.llmpanel.io/v1",
    api_key="lp_live_…",
)
client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "hi"}],
)

Self-Hosted

$0forever

Free forever. Run the full platform on your own hardware or cloud.

Open source, no strings attached

Deploy yourself

• Full platform, every feature included
• Unlimited deployments & models
• Bring your own GPUs or cloud keys
• OpenAI-compatible gateway & API keys
• Community support on GitHub

Founder pricingSolo

$19/mo

For one developer running a handful of models. We host the panel.

First 50 customers: $9/mo, forever

Start with Solo

• 4 deployments included
• $8/mo per extra deployment
• Single seat
• Hosted dashboard — no panel to babysit
• OpenAI-compatible gateway & API keys

Most popularTeam

$49/mo

For teams sharing a GPU fleet across one workspace.

Extra deployments just $5/mo each

Start with Team

• Everything in Solo
• 10 deployments included
• $5/mo per extra deployment
• Unlimited team members
• Shared workspace with roles & invites