Ridge

Ridge is a GPU cloud platform specialized in AI infrastructure, founded in 2023 in San Francisco by Igor Petrov, a former Google DeepMind engineer. The platform provides fast, scalable access to high-end GPUs for training and inference of artificial intelligence models.

What is Ridge?

Ridge is a GPU cloud provider built specifically for AI and machine learning workloads. Unlike generalist hyperscalers such as AWS, GCP, or Azure, Ridge focuses exclusively on GPU infrastructure, enabling pricing up to 50% lower for equivalent performance. Instances are available within minutes, with per-second billing.

Key Features

• High-end GPUs: Access to NVIDIA H100 (SXM and PCIe), A100 (80GB and 40GB), and L40S covering all use cases from LLM training to real-time inference
• Managed Kubernetes (RKS): CNCF-certified Kubernetes service with GPU auto-provisioning and native auto-scaling
• Serverless GPU: Deploy your models without managing infrastructure, with automatic scaling and pay-per-second billing
• Bare metal: For workloads requiring full control and minimal latency
• Fast NVMe storage: Up to 100 TB per node with native 400Gbps RDMA networking

Who is Ridge for?

Ridge primarily targets AI startups, data science teams, and mid-size companies that need GPU power without the prohibitive cost of hyperscalers. The platform is particularly suited for language model training, fine-tuning, and large-scale inference deployment.

Integrations and Compatibility

Ridge natively integrates with the cloud-native ecosystem: Kubernetes, Terraform, Docker, Jupyter Notebooks, and VS Code. The platform supports major frameworks like PyTorch and TensorFlow, and offers Weights & Biases integration for experiment tracking.

Conclusion

Ridge offers a credible and cost-effective alternative to cloud giants for AI workloads. With competitive pricing, fast provisioning, and an exclusive focus on GPU computing, the platform stands out as a strong choice for AI teams looking to optimize their infrastructure budget.