Documentation

LLM Hangar deploys open large language models onto GPU infrastructure in your own cloud account and gives you a private, key-authenticated, OpenAI-compatible endpoint. The infrastructure belongs to you: it runs in your account, under your provider agreement, and your provider bills you for it directly.

Quickstart

  1. Create an account and start the 24-hour trial.
  2. Connect a cloud account: AWS, Nebius, or RunPod.
  3. Deploy a model from the catalog. Pick a GPU shape and region, review the cost estimate, confirm.
  4. When the deployment is ready, open its Connect tab for the endpoint URL and API key.
  5. Point any OpenAI-compatible client at it.

Guides

Connect your cloud account

AWS via a CloudFormation cross-account role, Nebius via a project-scoped service account, RunPod via an API key. What each needs and what we verify.

Deploy a model

The deployment flow from catalog to running endpoint: shapes, regions, cost estimates, and what happens during a boot.

Use your endpoint

Working snippets for Python, curl, JavaScript, LangChain, the Vercel AI SDK, editors, and automation tools.

Budget caps and auto-destroy

Hard spend limits, self-destruct timers, wake/sleep schedules, and verified teardown.