Question: Is there a platform that helps reduce costs and increase efficiency in AI development by automating infrastructure provisioning and optimizing GPU usage?

If you're trying to cut costs and improve efficiency in AI development, dstack is worth a look. It automates the provisioning of infrastructure for AI model development, training and deployment on a variety of cloud services and on-premises servers. With its dev environments, tasks, services and pools, it makes it easy to set up AI workloads so you can concentrate on data and research while keeping costs low with cheap cloud GPUs.

Another contender is Anyscale, which promises the highest performance and efficiency for developing, deploying and scaling AI applications. It's based on the open-source Ray framework, but Anyscale supports a broader range of AI models. It's got features like smart instance management, heterogeneous node control and GPU and CPU fractioning to make the most of your resources. And it cuts costs by up to 50% with spot instances.

If you need a cloud-based option, RunPod offers a globally distributed GPU cloud that lets you provision GPU pods and run serverless ML inference. It's got a range of GPUs and bills by the minute, so you can mix and match to match your needs. And it's got more than 50 preconfigured templates for frameworks like PyTorch and Tensorflow, so setup and management is easy.

If you're looking for a service that'll scale and optimize costs, check out Cerebrium. This serverless GPU infrastructure is for training and deploying machine learning models, with pay-per-use pricing and features like GPU variety, infrastructure as code and real-time logging and monitoring. It's designed to scale automatically without latency or high failure rates, so it's a good option if you want to keep costs low while still getting the results you need.