GPU Optimization & Cost Control • Self‑Hosted

The Infrastructure Layer for Private LLMs

Run and monitor your own AI models behind the firewall. Cut GPU spend by up to 40% with autoscaling, idle detection, and real‑time FinOps — without cloud lock‑in or data risk.

🚀 Live Demo 💡 Start Free

Keep Data In‑House

No external APIs. No data egress. Your models, your VPC or on‑prem.

Slash GPU Waste

Visibility and automation prevent idle spend and over‑provisioning.

Ship Faster

Prebuilt templates and a unified control plane beat DIY pipelines.

Core Features

Everything you need to deploy, scale, observe, and save.

🚀 Automated Model Deployment

Deploy and manage LLaMA, Mistral, Mixtral, and more—with no DevOps headaches.

⚙️ Intelligent Resource Management

Auto‑scale GPU/CPU/RAM, detect idle instances, and route workloads smartly.

📊 Cost Control & FinOps

Real‑time spending, alerts, forecasting, and per‑team attribution.

🔍 Monitoring & Observability

Latency/throughput, resource utilization, error logs, and version history.

🔐 Enterprise‑Grade Security

Self‑hosted, air‑gap, SSO/RBAC, audit logs, and zero persistent telemetry.

🧩 Pluggable Runtimes

vLLM, TGI, Triton, Ray, Hugging Face—choose your stack.

Slash GPU Costs Without Slowing Down

Most teams overspend 20–50% on idle or mis‑allocated GPU capacity. LLM-Stack gives you the visibility and automation to eliminate waste in days — not quarters.

Idle GPU Detection

Auto‑shutdown for unused nodes. Wake on request to keep latency low.

Intelligent Routing

Send inference to the most cost‑efficient node or runtime, automatically.

Real‑Time FinOps

Per‑model & per‑team spend, alerts, and forecasting to prevent overruns.

ROI Fast Lane: Most teams recover the subscription cost in GPU savings within the first month.

How It Works

STEP 1

Install in Your Infra

Docker or Helm. Air‑gapped compatible. No external dependencies.

STEP 2

Deploy a Model

Use templates to launch LLaMA/Mistral with your preferred runtime.

STEP 3

Optimize & Observe

Track cost/latency, scale intelligently, and enforce governance.

Supports vLLM, TGI, Triton, Ray, and Hugging Face stacks. Swap without rewriting your app.

Install in Minutes

# Docker (quick start)
docker run -d --name llmstack -p 8080:8080 ghcr.io/llmstack/llmstack:latest

# Helm (Kubernetes)
helm repo add llmstack https://charts.llmstack.dev
helm install llmstack llmstack/llmstack --namespace llmstack --create-namespace

# Authenticate (local admin)
llmstack login --host http://localhost:8080

Replace registry/host as needed. Air‑gapped offline bundle available.

How We Compare

Feature	LLM-Stack	Modal	Anyscale	RunPod	BentoML
Self‑hosted / Air‑gapped	✅	❌	⚠️	❌	⚠️
Pluggable runtimes	✅	❌	✅	❌	✅
Built‑in FinOps / cost savings	✅	❌	⚠️	❌	❌
No vendor lock‑in	✅	❌	⚠️	❌	✅
UI + API/CLI	✅	✅	✅	⚠️	⚠️

Pricing

Most teams recover the subscription via GPU savings in the first month.

Startup

$199/mo

• Up to 3 models
• Limited infrastructure
• Basic dashboards

💡 Offset 100% via idle GPU reductions.

Growth

$999/mo

• 4–10 models
• Advanced dashboards
• Automation & scaling

💰 Typical savings: 20–40% within 30 days.

Enterprise

Contact for Pricing

• Multi‑node orchestration
• Granular cost reporting
• Team‑level controls

📉 Savings compound with larger fleets.

Start Free

“We stood up self‑hosted LLMs in days instead of months, and cut idle GPU spend by 38% in the first month.”

— Head of ML, Mid‑Market FinTech

FAQ

Do you host or manage our models?

No. LLM-Stack is software that runs in your environment. Your data and models stay fully under your control.

Which runtimes and models are supported?

vLLM, TGI, Triton, Ray, and others. Launch LLaMA, Mistral, Mixtral, Gemma, and more via templates.

Can we run in air‑gapped environments?

Yes. No outbound telemetry by default and offline install options are available.

How does LLM-Stack save us money?

By preventing idle GPU spend, autoscaling capacity to match load, routing inference to lower‑cost nodes, and giving finance/engineering shared visibility with alerts and forecasting.