Question: I'm looking for a tool that provides a reliable coding environment with automated cloud infrastructure and fine-grained GPU usage control.

If you're looking for a coding environment with automated cloud infrastructure and the ability to control GPU usage at a fine level, Zerve is worth a serious look. It's a notebook-IDE hybrid that supports multiple programming languages and has unlimited parallelization. The service can be self-hosted on AWS, Azure or GCP, so you have full control over your data and infrastructure. Zerve is geared for data science teams that need to balance collaboration and stability, with a free community plan and custom Enterprise plans with more advanced security and organizational controls.

Another strong contender is Anyscale, which offers a platform for building, deploying and scaling AI applications. It supports a broad range of AI models, has workload scheduling and heterogeneous node control. Anyscale's cloud flexibility and smart instance management means you can optimize resources, with customers reporting up to 50% cost savings on spot instances. It integrates with popular IDEs and has strong security controls, too, so it's a good fit for enterprise customers.

If you need a globally distributed GPU cloud, RunPod offers a service that lets you spin up GPU pods on demand. It supports multiple GPUs, serverless ML inference and instant hot-reloading. With autoscaling and job queuing, RunPod makes it easier to develop, train and run AI models. It also offers a CLI tool for easy provisioning and deployment, so it's a good fit for developers.

Another flexible option is dstack, an open-source engine that automates infrastructure provisioning for AI models. It supports multiple cloud providers and on-prem servers, making it easy to set up and manage AI workloads. dstack offers multiple deployment options and a wealth of community support, making it a good option for developers and researchers on a budget.