> For the complete documentation index, see [llms.txt](https://docs.vectra.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.vectra.ai/deployment/getting-started/why-migrate-to-rux-from-qux.md).

# Why migrate to RUX from QUX

Reasons to move to a RUX deployment (Vectra AI Platform) from a QUX deployment (appliance deployment)

The Vectra AI Platform is the integrated signal powering XDR providing hybrid attack surface coverage across identity, public cloud, SaaS, and data center networks; AI-driven Attack Signal Intelligence that prioritizes real attacks in real-time; and integrated, automated, and managed response to move at the speed and scale of hybrid attackers.

**Why Migrate to the Vectra AI Platform with Respond UX:**\
This document provides customers an overview of the Vectra AI Platform (with Respond UX) in comparison to the Vectra's appliance-based Detect platform (with Quadrant UX). In this document you will learn the differences between the two offerings and why migrating to the Vectra AI Platform promises to deliver additional value to your organization.

If you would like to migrate to the Vectra AI Platform with Respond UX, reach out to your account team.

**Vectra AI Platform Migration Guide:**\
We have made the migration process to the Vectra AI Platform simple and straightforward. Please see the [Migration to RUX from QUX](/deployment/getting-started/migration-to-rux-from-qux.md) article for details on the migration process.

### Attachments

{% file src="/files/R1IWXKfhzRIGbeGK9llk" %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.vectra.ai/deployment/getting-started/why-migrate-to-rux-from-qux.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
