This topic surveys the current landscape of cloud GPU providers and managed GPU services used to train and serve generative AI models, and how platform integrations streamline deployment and runtime operations. As of 2026, demand for large-model training and low-latency inference keeps GPU capacity, interconnect performance and software stacks central to cloud selection. Specialist clouds (e.g., CoreWeave and NVIDIA-partnered offerings) focus on high-density, cost-effective GPU instances and GPU-optimized stacks; hyperscalers (AWS, Google Cloud) combine broad managed services, global networking and ecosystem integrations for production pipelines. Practical platform integrations are increasingly important: infrastructure glue such as Model Context Protocol (MCP) servers and cloud-specific MCP adapters let LLM-based agents provision instances, deploy containers, run SQL or Python, and manage datasets without manual cloud console workflows. Example integrations include AWS MCP servers that expose S3/DynamoDB and sandboxed boto3 execution for secure resource operations, an Athena MCP server for SQL queries over Glue Catalog data, and a Cloud Run MCP server to deploy containerized model endpoints. Specialized tools like GibsonAI aim to streamline database and data-migration tasks for ML datasets. These components illustrate how model orchestration, data access, and compute provisioning are being automated. Choosing a provider hinges on workload profile (training vs inference), model size, latency needs, and integration requirements: spot/ephemeral vs guaranteed capacity, managed inference endpoints, and ready-made SDKs and MCP adapters. This overview helps teams weigh performance, operational automation, and cost when integrating GenAI workloads across CoreWeave, NVIDIA-backed clouds, AWS, and Google Cloud.