> For the complete documentation index, see [llms.txt](https://airas.gitbook.io/airas/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://airas.gitbook.io/airas/products/airas-cloud.md).

# Airas Cloud

### Overview

Airas Cloud is our scalable compute service for data-intensive blockchain and AI workloads. Whether you’re training a model to predict real estate market shifts or analyzing massive sets of asset data, Airas Cloud provides the power you need without the complexity.

### Key Features

* **On-Demand Nodes**\
  Spin up environments in minutes, perfect for short bursts or continuous operation.
* **High Performance**\
  Optimized infrastructure to handle everything from AI training to large-scale simulations.
* **Cost-Effective Plans**\
  Pay-as-you-go model so you’re only charged for the resources you actually use.
* **Seamless Integration**\
  Works hand-in-hand with Airas Agent and Assistant for a complete AI + blockchain toolchain.

### Getting Started

1. **Sign Up**\
   Create an account on Airas Cloud (details coming soon).
2. **Choose a Node**\
   Select the type of node based on your workload (GPU-heavy for AI training, CPU-focused for analytics, etc.).
3. **Deploy**\
   In a few clicks, your node is live. Connect via SSH or integrate directly with Airas Agent or Assistant.
4. **Monitor**\
   Use the dashboard to track performance, usage, and costs in real-time.

### Usage Examples

* **Bulk Data Analysis**\
  Offload property valuation calculations or big-data tasks.
* **AI Model Training**\
  Train or fine-tune models for price predictions or risk assessments.
* **Live dApp Hosting**\
  Keep your real asset marketplace online with reliable node infrastructure.

### Next Steps

* Combine **Airas Cloud** with **Airas Agent** for large-scale scraping or data gathering.
* Use **Airas Assistant** to interpret analytics results and present them in a human-readable format.
* Experiment with multi-node deployments for parallel processing and faster computations.


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