> For the complete documentation index, see [llms.txt](https://docs.stg.ai.neevcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.stg.ai.neevcloud.com/ai-storage/overview.md).

# Overview

NeevCloud provides you with flexible storage solutions designed specifically for GPU-intensive AI/ML workloads. You can choose between temporary and persistent storage options, allowing you to optimize for both performance and cost based on your specific use case.

## Why Use NeevCloud Storage?

When you're running AI/ML experiments, training models, or deploying inference pipelines, you need storage that adapts to your workflow. NeevCloud storage gives you:

* **Flexibility in data persistence**: You decide whether your data should survive after your GPU instance terminates.
* **Performance options**: Choose between ultra-low latency local storage or shared network storage.
* **Cost transparency**: You pay only for what you use with clear per-GB monthly pricing.
* **Simple integration**: Your storage mounts automatically and works seamlessly with SSH, Jupyter Notebook, and Comfy UI.

## Key Features

**Persistent and Ephemeral Options**\
You can select storage that either persists beyond your instance lifecycle or gets automatically cleaned up when you're done.

**Multiple Storage Types**\
Local disk storage gives you the fastest possible I/O for single-node operations, while network storage enables multi-node access and data sharing across your team.

**Retention Policies (Coming Soon)**\
You'll soon be able to define exactly how long your data remains accessible after GPU deletion, giving you more control over storage lifecycle management.

**Direct Instance Access**\
All your storage mounts automatically at `/data` on your GPU instance, so you can start working immediately without complex setup procedures.

## Who Should Use Storage

* ML teams storing datasets and models
* Developers managing application data
* Enterprises running long-lived AI projects
* Teams needing shared data across workloads

## How Storage Works

* Storage can be mounted to GPU instances during deployment.
* Persistent storage remains available even if the compute instance is stopped or deleted.
* Ephemeral storage is tied to the instance lifecycle and is deleted when the instance is deleted.