> ## Documentation Index
> Fetch the complete documentation index at: https://agent-observability-docs.splunk.com/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 Splunk Agent Observability and is deployed by Splunk Agent Observability into your own cluster or cloud.

<Note>Luna Studio is only available in the **Enterprise tier of Splunk Agent Observability** and is deployed by Splunk Agent Observability into your own cluster or cloud. [Contact us](https://www.splunk.com/en_us/about-splunk/contact-us.html) to learn more and get started.</Note>

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

## Who can use Luna Studio

Luna Studio is available to **enterprise customers of Splunk Agent Observability**.

* There is no public sign-up at a shared `app.luna-studio.ai`-style URL.
* Each customer organization reaches Luna Studio at the URL provisioned for them, typically alongside the Splunk Agent Observability UI.
* Once provisioned, anyone in the customer's organization 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 Splunk Agent Observability today and you want access to Luna Studio, [contact us](https://www.splunk.com/en_us/about-splunk/contact-us.html).

## How Luna Studio is deployed

Splunk Agent Observability 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`

The GPU fine-tuning jobs run inside your environment against the capacity and quotas your team manages. Synthetic data generation is orchestrated by Luna Studio but calls the supported runtime LLM provider and model you select.

## 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 Splunk Agent Observability contact.

| Layer                                                    | Examples                                                                                                                                         | Who configures it                                                                                                                                           |
| -------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Deployment-level**                                     | Training platform (Kubernetes, Vertex AI Pipelines, AzureML Pipelines, SageMaker Pipelines), object storage, PostgreSQL, Luna base model catalog | Splunk Agent Observability or your deployment operator, at deployment time                                                                                  |
| **Runtime (in-app)**                                     | OpenAI, Anthropic, Google Gemini, and Groq credentials; Splunk Agent Observability integration records; projects and runs                        | Individual users through personal overrides, or administrators through workspace defaults on the [Integrations page](/luna-studio/ui/integrations/overview) |
| **Current-release Splunk Agent Observability exception** | Effective Splunk Agent Observability API URL and key for dataset import and evaluator registration                                               | Your deployment operator until the current backend limitation is addressed                                                                                  |

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 includes the supported LLM keys and Splunk Agent Observability records users configure in the app, plus projects and runs.

The Splunk Agent Observability exception is current implementation behavior, not a permanent architectural classification: although personal and workspace-scoped `galileo_api` records can be saved in the UI, dataset import and evaluator registration currently ignore both and read deployment settings instead. See [Splunk Agent Observability integration](/luna-studio/ui/integrations/splunk-ao) for the operational setup and verification path.

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://www.splunk.com/en_us/about-splunk/contact-us.html">
    Talk to Splunk Agent Observability about enabling Luna Studio for your organization.
  </Card>

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