If you're looking for a managed Kubernetes platform that spans multiple cloud providers and supports fast inference engines, Mystic could be a good fit. Mystic is designed to make it easy to deploy and scale Machine Learning models with serverless GPU inference. It supports multiple inference engines and works with AWS, Azure and GCP. The platform provides a managed Kubernetes environment, automated scaling and cost optimization features like spot instances and cloud credits, so it's both cheap and scalable.
Another good option is Anyscale. It's based on the open-source Ray framework and supports a broad range of AI models, including LLMs and custom generative AI models. Anyscale offers cloud flexibility across multiple clouds and on-premise environments, along with smart instance management and optimized resource utilization. It also comes with native integrations with popular IDEs, persisted storage and Git integration that can help you get a lot more out of your workflows for building and deploying AI applications.
If you're looking for a cost-effective option with abundant GPU resources, Salad offers a cloud-based platform for deploying and managing AI/ML production models. It can handle a range of GPU-hungry workloads and offers a fully-managed container service, a global edge network and on-demand elasticity. With multi-cloud support and SOC2 certification, Salad promises security and reliability, and its pricing starts at $0.02/hour for GTX 1650 GPUs, with discounts for large-scale usage.
Last, RunPod offers a globally distributed GPU cloud for developing, training and running AI models. It lets you spin up GPU pods on demand, offers serverless ML inference and supports more than 50 preconfigured templates for popular AI frameworks. With features like autoscaling, job queuing and instant hot-reloading for local changes, RunPod makes it easy to deploy and manage AI models, with pricing that varies depending on the type of GPU instance you use.