> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Availability and deployment

> Luna Studio is part of the enterprise tier of Galileo and is deployed by Galileo into your own cluster or cloud.

<Note>Luna Studio is only available in the **Enterprise tier of Galileo** and is deployed by Galileo into your own cluster or cloud. [Contact us](https://galileo.ai/contact-sales) to learn more and get started.</Note>

Luna Studio is the self-service fine-tuning web app for [Luna-2](/concepts/luna/luna) custom evaluation metrics. Because Luna-2 itself is an enterprise-tier capability, Luna Studio inherits the same availability model — it ships as part of an enterprise Galileo deployment, not as a shared SaaS product.

## Who can use Luna Studio

Luna Studio is available to **enterprise customers of Galileo**.

* There is no public sign-up at a shared `app.luna-studio.ai`-style URL.
* Each customer org reaches their Luna Studio at the URL Galileo provisions for them (typically alongside their Galileo console).
* Once provisioned, anyone in the customer's org can sign in, create projects, and launch training runs — see the [Quickstart](/luna-studio/ui/quickstart).

If your team is on the standard tier of Galileo today and you want access to Luna Studio, [contact us](https://galileo.ai/contact-sales).

## How Luna Studio is deployed

Galileo provisions Luna Studio into the customer's own infrastructure. The training jobs that fine-tune Luna base models run on a training platform chosen at deployment time:

* `Kubernetes`
* `Vertex AI Pipelines`
* `AzureML Pipelines`
* `SageMaker Pipelines`

This means the heavyweight compute — both the inference for synthetic data generation and the GPU fine-tuning jobs — runs inside your environment, against the GPUs and quotas your team already manages.

## What's fixed at deployment vs. configurable in-app

Luna Studio splits its integrations into two layers. Understanding which layer a setting lives on tells you whether you change it in the UI or talk to your Galileo contact.

| Layer                | Examples                                                                                                             | Who configures it                                                                     |
| -------------------- | -------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| **Deployment-level** | Training platform (Kubernetes, Vertex AI Pipelines, AzureML Pipelines, SageMaker Pipelines), Luna base model catalog | Galileo, at deployment time                                                           |
| **Runtime (in-app)** | LLM provider integrations (OpenAI, Anthropic, Azure, Vertex AI, AWS, custom), Galileo integration, projects, runs    | Your org's users, from the [Integrations page](/luna-studio/ui/integrations/overview) |

The deployment-level layer is what these docs mean when they reference "your Luna Studio deployment" — for example, the [base model](/luna-studio/ui/core-concepts#base-models) list shown in Step 4 of the new run flow is sourced from your deployment. The runtime layer is everything users configure inside the app and is the focus of the rest of these docs.

For more on the runtime layer, see [Integrations overview](/luna-studio/ui/integrations/overview). For more on the training platform list, see [Training platforms](/luna-studio/ui/integrations/overview#training-platforms).

## How to get Luna Studio

<CardGroup cols={2}>
  <Card title="Contact sales" icon="comment" href="https://galileo.ai/contact-sales">
    Talk to Galileo about enabling Luna Studio for your org.
  </Card>

  <Card title="Quickstart" icon="rocket" href="/luna-studio/ui/quickstart">
    Already provisioned? Walk through your first end-to-end training run.
  </Card>
</CardGroup>