Question: Is there a platform that provides a full end-to-end solution for AI application development, deployment, and scaling, with support for heterogeneous nodes and GPU/CPU fractioning?

If you want a full-on platform for developing, deploying and scaling AI, Anyscale is a strong contender. It can control heterogeneous nodes and fractionally allocate GPUs and CPUs for efficient use of resources. It's based on the Ray framework, but also can be used with other IDEs and supports native support for many AI models, including LLMs. The company offers features like workload scheduling, intelligent instance management and cost optimization with spot instances, making it a good choice for enterprise customers.

Another contender is Salad, a cloud-based service to deploy and manage AI/ML production models. It includes a managed container service, global edge network and on-demand elasticity, which is good for GPU-hungry workloads like computer vision and language models. Salad spans multiple cloud environments and has SOC2 certification for better security and reliability, and costs start at $0.02/hour for GTX 1650 GPUs.

If you want an everything-and-the-kitchen-sink AI package, the NVIDIA AI Platform is a full stack for training, development and deployment. It includes multi-node training with NVIDIA DGX Cloud and automates the entire AI workflow with its AI platform software and models. The platform can help businesses scale AI applications, build AI into operations and lower total cost of ownership.

Last is dstack, an open-source engine that automates infrastructure provisioning for AI workloads running on multiple cloud providers and on-prem servers. It can efficiently manage AI workloads with features like dev environments and tasks, so you can focus on data and research instead of infrastructure. dstack is very flexible when it comes to deployment, with options including self-hosted and managed services.