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VeroCloud

Cost-Effective AI Cloud Platform for Development & Scaling

Usage Based
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Description

VeroCloud provides a specialized cloud infrastructure platform focused on affordability and performance for demanding workloads like Artificial Intelligence (AI) and High-Performance Computing (HPC). The platform enables users to seamlessly develop, deploy, and scale their applications, offering significant cost savings (up to 70%) and performance efficiency improvements (up to 15X) compared to traditional providers. It guarantees high availability with 99.99% uptime.

Core services include scalable GPU Cloud instances featuring a wide range of NVIDIA GPUs (like A100, H100, H200, L40S, A30), Serverless AI Inference with features like autoscaling and sub-250ms cold starts, HPC Compute solutions, Bare Metal servers, and support for deploying any container via public or private image repositories. VeroCloud provides optimized environment templates for quick setup and allows users to create custom templates for specific needs, ensuring flexibility for various machine learning workflows and other compute-intensive tasks. The platform emphasizes security and reliability, with upcoming certifications like SOC 2 and ISO 27001 planned.

Key Features

  • Cost-Effective Cloud: Claims up to 70% cost savings on AI cloud solutions.
  • High Performance GPUs: Offers a wide range of NVIDIA GPUs (A30, A40, L4, A5000, 3090, L40, L40S, 6000 Ada, A6000, A100, H100, H200).
  • Serverless AI Inference: Provides autoscaling, job queueing, and sub-250ms cold start times for AI models.
  • HPC Workload Support: Enables seamless deployment of High-Performance Computing tasks.
  • Guaranteed Uptime: Offers 99.99% guaranteed uptime for services.
  • Customizable Environments: Supports pre-configured templates, custom templates, and container deployment.
  • Scalability: Designed to support growth from a few users to millions.
  • Real-Time Metrics: Access to statistics like method calls and response times.

Use Cases

  • Developing and training AI models
  • Deploying and scaling AI inference applications
  • Running large language models (LLMs)
  • Executing High-Performance Computing (HPC) simulations
  • Deploying containerized applications on cloud infrastructure
  • Hosting scalable cloud servers
  • Provisioning bare metal servers for specific needs

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