Cloud GenAI acceleration and orchestration platforms focus on turning research models and prototypes into repeatable, governed production services by combining scalable data platforms, multi‑model hosting, and agent orchestration. As enterprises pair cloud stacks (for example, Unicore on AWS or Snowflake’s integrations with Anthropic-style models) they aim to reduce friction between data, models, and runtime agents while adding observability, governance and cost controls. This topic spans three practical layers: AI Data Platforms (Snowflake and similar systems that centralize datasets, feature stores and secure access), AI Tool Marketplaces (Agentverse and other registries that let teams list, discover and reuse agent components and connectors), and Agent Frameworks (LangChain’s engineering stack and LangGraph state management, AutoGPT/AgentGPT autonomous agent runners, and developer-focused tools like Windsurf, Warp and IDE‑integrated agents). Enterprise assistants such as Microsoft 365 Copilot and IBM watsonx Assistant illustrate production use cases where orchestration and data plumbing are essential. Key trends include orchestration-first designs that coordinate retrieval, model selection and action execution; multi-model and multi-vendor model routing to optimize accuracy, latency and cost; marketplaces and registries for composable agent skills; and stronger operational controls—logging, provenance, evaluation and safety checks—needed for regulated deployments. For practitioners, evaluating stacks means considering data locality, model interoperability, runtime orchestration features, developer tooling, and marketplace ecosystems that accelerate reuse without sacrificing governance. This convergence makes cloud GenAI platforms a practical foundation for moving agentic applications from pilots to production at scale.