Question: I'm looking for a platform that can help me scale my AI applications efficiently and cost-effectively.

If you need a platform to run your AI workloads at scale and at the lowest cost, Anyscale is worth a serious look. It offers the highest performance and efficiency with workload scheduling, cloud flexibility, smart instance management and optimized resource allocation. Based on the open-source Ray framework, Anyscale supports a broad range of AI models and can cut costs by up to 50% compared to spot instances. It also comes with native integrations with popular IDEs and offers strong security and governance controls.

Another strong contender is Salad, a cloud-based service focused on running and managing AI/ML production models at scale. Salad is a cost-effective way to run thousands of consumer GPUs around the world, with features like on-demand elasticity, multi-cloud support and a global edge network. It supports GPU-heavy workloads like text-to-image and speech-to-text, and pricing starts at $0.02/hour for GTX 1650 GPUs, with deep discounts for large-scale usage.

For rapid development and deployment of generative AI models, check out Together. It comes with optimizations like Cocktail SGD and FlashAttention 2 to accelerate model training and inference. Together supports a variety of AI workloads and offers scalable inference, collaborative tools for fine-tuning, and deep cost savings compared to traditional providers. It's geared for companies that want to build private AI models into their products.

Last, RunPod is a globally distributed GPU cloud that lets you run any GPU workload. It offers instant GPU pod spinning up, serverless ML inference and support for frameworks like PyTorch and Tensorflow. With no egress or ingress charges and a flexible pricing model, RunPod is a good option for running and training AI models.