

Clust is a cloud infrastructure platform that allows users to rent GPU instances on an hourly basis. It is designed for developers and engineering teams who require processing power for AI and machine learning workloads.
The platform offers several GPU options, including the A100 80GB, RTX 4090, and V100, which can be launched via a graphical user interface (GUI) or a command-line interface (CLI). It supports the use of pre-built Docker images to help users set up their ML stacks.
Buyers should confirm whether they require consistent availability or if their budget allows for interruptible spot pricing, which provides lower rates. Users can also choose from different data center tiers to match their security requirements.
Provides consistent pricing for GPU rentals based on hourly rates.
An auction-based pricing model that may reduce costs by over 50% for flexible workloads.
Includes images for Python, TensorFlow, and Jupyter to support ML stack setup.
Includes scriptable filters and the ability to launch instances via a graphical interface or command line.
Offers security levels ranging from hobbyist setups to Tier-4 data centers.
Providing processing power to train AI models.
Using GPUs to adjust and optimize existing AI models.
Hosting and deploying AI applications using cloud instances.
Using GPU power for video rendering and high-resolution visual processing.
Hourly pricing ranges from $0.14 to $2.21. Spot pricing may reduce costs by over 50% compared to on-demand rates.
Clust provides access to several GPUs, including the RTX A4000, RTX A5000, V100, RTX 3090, RTX 4090, A40, L40, and A100 80GB.
On-demand pricing provides consistent costs, while interruptible spot-based pricing may save users over 50%.
Yes, the platform is designed for AI and ML workloads, specifically supporting the training, fine-tuning, and deployment of AI models.
Source category: Software Development
Source subcategory: Cloud Infrastructure
Clust is a GPU cloud rental platform for software companies and AI developers. It supports AI/ML workflows such as training and deploying models through on-demand and spot instances. Buyers should choose between consistent on-demand pricing or lower-cost interruptible spot instances based on their project needs.