If you need a cloud service to rapidly provision GPU instances for machine learning model development, RunPod is a good choice. It's a globally distributed GPU cloud service that lets you spin up GPU pods on demand. The service supports a range of GPUs and bills by the minute with no egress or ingress charges. It also offers serverless ML inference with autoscaling, instant hot-reloading, and more than 50 preconfigured templates for frameworks like PyTorch and Tensorflow.
Another good option is Anyscale, which offers a service for developing, deploying and scaling AI applications. It offers workload scheduling with queues, cloud flexibility across multiple clouds and on-premise, and smart instance management. Anyscale supports a wide range of AI models and offers cost savings of up to 50% on spot instances. It also offers native integrations with popular IDEs and persisted storage, so it's a good option for machine learning model development.
Mystic is another option. It's a serverless GPU inference service that works directly with AWS, Azure and GCP. Mystic offers cost optimization options like spot instances and parallelized GPU usage. It comes with a managed Kubernetes environment and an open-source Python library to make it easier to deploy and scale machine learning models.
If you're looking for a cost-effective option, Salad offers a cloud-based service for deploying and managing AI/ML production models at scale. It supports a range of GPU-hungry workloads and offers a fully-managed container service with on-demand elasticity. Salad is designed for scalability and security, so it's a good option for teams that want to cut costs without sacrificing performance.