This topic covers how large organizations deploy generative AI by combining enterprise data platforms with managed model partnerships — for example, integrating Snowflake’s data and marketplace capabilities with third‑party models from Anthropic, OpenAI and others. The focus is on practical concerns: secure access to enterprise data, model selection and orchestration, governance and compliance, developer workflows, and the role of curated marketplaces and managed service agreements in reducing integration risk. Relevance and timing (late 2025): enterprises are moving beyond pilot projects to production GenAI use cases that demand consistent governance, data residency controls, and predictable operational profiles. Managed model partnerships and platform‑level marketplaces shorten time to value by providing vetted models, billing/SLA frameworks, multi‑model access and prebuilt connectors to enterprise systems — but they introduce tradeoffs around vendor lock‑in, provenance and cost control. Key tools and categories: Snowflake and similar AI data platforms act as repositories, governance layers and integration hubs; Anthropic’s Claude family and OpenAI’s APIs provide conversational and developer‑oriented model families; Microsoft 365 Copilot, IBM watsonx Assistant and Harvey illustrate enterprise assistants and domain‑specific platforms for business workflows; GitHub Copilot, Windsurf (formerly Codeium), Tabnine and Salesforce CodeT5 represent developer‑focused coding models and agentic tooling. Together these categories — AI data platforms and AI tool marketplaces — enable secure model consumption, indexing of enterprise knowledge, and embedding capabilities into apps and developer workflows. Practical considerations: prioritize data governance, multi‑model and private‑deployment options, model provenance and SLAs, integration cost and latency, and developer experience. Evaluating managed partnerships means weighing operational simplicity against control, compliance and long‑term extensibility.