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.
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