How to Get Started

Discover Kloud Team's GPU and CPU compute resources for your AI workloads

Welcome to Kloud Team

Kloud Team provides flexible cloud compute resources tailored for AI and machine learning workloads. Choose from our high-performance GPU shards or scalable CPU instances to power your applications.

Quick Start

Create your first pod

Step-by-step guide to launch your first pod and deploy your AI workload.

Configuration

SSH

Manage SSH keys for secure access to your pods on Kloud Team.

Networking

HTTP Services

Access your pod services via HTTP proxy with automatic URL exposure.

AI Models

AI & Machine Learning

Browse and deploy AI models from various providers on Kloud Team.

Our Services

Kloud Team offers two types of compute resources to meet your workload requirements:

GPU Shards

High-performance GPU resources for demanding AI workloads, training models, and inference:

  • 1g.10gb – Entry-level GPU shard with 10GB memory
  • 2g.20gb – Mid-tier GPU shard with 20GB memory
  • 3g.40gb – Advanced GPU shard with 40GB memory
  • 7g.80gb – Premium GPU shard with 80GB memory

Perfect for machine learning training, deep learning inference, and compute-intensive applications.

CPU Resources

Scalable Intel CPU instances for general-purpose computing and web applications:

  • Intel (2vCPU.4gb) – Lightweight workloads and development
  • Intel (4vCPU.8gb) – Standard applications and services
  • Additional configurations available for larger workloads

Ideal for web services, APIs, data processing, and non-GPU workloads.

Why Choose Kloud Team?

  • Flexible Resources – Scale your compute power up or down based on demand
  • GPU Access – Access to high-performance GPU shards for AI workloads
  • Simple Pricing – Pay only for what you use with transparent pricing
  • Fast Deployment – Launch your workload in minutes
  • Easy Management – Monitor and manage resources from a single dashboard

Getting Started

Ready to launch your first workload? Follow these steps:

  1. Choose your compute resource – Select from GPU shards or CPU instances
  2. Configure your environment – Set up your runtime and dependencies
  3. Deploy your workload – Launch your application or model
  4. Monitor and scale – Track usage and adjust resources as needed

Start with the Create your first pod guide to get up and running.

Need Help?

Have questions or need support? Reach out to us: