> For the complete documentation index, see [llms.txt](https://pingo-work.gitbook.io/pingo/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://pingo-work.gitbook.io/pingo/overview/why-pingo/pain-points-and-solutions.md).

# Pain Points and Solutions

## <mark style="background-color:blue;">1.</mark> <mark style="background-color:blue;"></mark><mark style="background-color:blue;">**Idle and Fragmented Computing Power Availability**</mark>

Many data centers and decentralized storage providers possess substantial idle CPU capacity. PinGo tackles this issue by aggregating these underutilized resources into a decentralized network. This approach maximizes their utilization and offers a cost-effective solution for AI and machine learning needs.

## <mark style="background-color:blue;">**2. Scalability and Rapid Clustering**</mark>

Traditional cloud providers often struggle with efficiently and quickly clustering CPU resources across various locations. PinGo’s CDN infrastructure enables the rapid formation of global CPU clusters, significantly enhancing scalability and reducing setup times for large-scale AI applications.

## <mark style="background-color:blue;">**3. High Costs of Traditional Cloud Computing**</mark>

The expense of renting CPU power from centralized cloud providers like AWS and Google Cloud can be prohibitively high. PinGo provides a decentralized alternative that leverages underutilized CPU resources, reducing costs and making computational power more affordable.

## <mark style="background-color:blue;">**4. Lack of Customization and Flexibility in Enterprise Solutions**</mark>

Traditional cloud services frequently lack the flexibility to cater to specific computational requirements. PinGo's user interface allows for detailed customization of CPU resources, locations, and security parameters, offering tailored solutions for various AI and machine learning projects.