If you're looking for a cloud platform with low-latency nodes and on-demand elasticity for distributed computing jobs, Salad is a strong candidate. Salad offers on-demand elasticity, a fully-managed container service, and a global edge network. It supports a variety of GPU-intensive workloads, including AI and ML models, with costs up to 90% lower than traditional providers. The platform also integrates with popular container registries and supports industry-standard tooling and Kubernetes workflows, making it a cost-effective solution for large-scale deployments.
Another excellent option is Anyscale, a platform designed for developing, deploying, and scaling AI applications. It features workload scheduling with queues, cloud flexibility across multiple clouds and on-premise environments, and smart instance management. Anyscale also supports heterogeneous node control and GPU and CPU fractioning for optimized resource utilization, which can lead to significant cost savings. The platform includes native integrations with popular IDEs and supports a wide range of AI models, making it a versatile choice for AI workloads.
For those who need immediate GPU availability and serverless ML inference, RunPod offers a globally distributed GPU cloud. The service enables the spinning up of GPU pods quickly and offers various billing models based on usage. RunPod also supports more than 50 preconfigured templates for frameworks like PyTorch and TensorFlow, along with custom containers, and provides a CLI tool for easy provisioning and deployment.
Finally, consider Gcore, a cloud and edge platform that accelerates AI training, delivers content, and protects servers and applications. Gcore offers a globally distributed network with low latency, high-performance computing resources, and various security features. The platform is highly customizable and includes services such as Edge Cloud, Edge Network, and Managed Kubernetes, making it suitable for a wide range of applications from gaming to healthcare.