This topic examines two prevailing architectural patterns for enterprise GenAI stacks: cloud-native integrations that pair model providers (e.g., Anthropic’s Claude) with data platforms like Snowflake and AWS managed services, versus hybrid/container-based solutions built on Red Hat (OpenShift) combined with AWS infrastructure. It’s timely as of late 2025 because organizations are balancing rapid GenAI adoption with tighter regulatory scrutiny, data-residency requirements, and higher expectations for security, observability, and model governance. Cloud-native Anthropic+Snowflake+AWS stacks prioritize managed APIs, elastic compute, and integrated data-to-model workflows: Snowflake functions as the canonical enterprise data platform and vector store for retrieval-augmented generation (RAG), Anthropic’s Claude family provides hosted LLM capacity and conversational assistants, and AWS supplies identity, key management, serverless orchestration, and scalable inference. This model accelerates time-to-value for analytics, assistants, and RAG apps. Red Hat + AWS solutions emphasize hybrid/hypervisor-level control and portability: OpenShift and related operators enable containerized model serving, on-prem or multi-cloud deployments, and integration with enterprise CI/CD and compliance tooling. This approach suits regulated industries that need stricter data residency, offline inference, or custom model control. Complementary tooling shapes both patterns: engineering frameworks like LangChain support agent orchestration and RAG pipelines; platforms such as MindStudio, Cimba.AI, and IBM watsonx Assistant enable no‑code/low‑code agent development and governance; GitHub Copilot and specialist vendors like Harvey highlight verticalized assistant use cases. Governance and security controls — policy enforcement, audit logging, model lineage, and access controls — are central to both architectures. Choosing between these stacks depends on trade-offs in velocity, control, and compliance; many enterprises adopt hybrid combinations that leverage managed cloud services for experimentation while standardizing production on containerized, governed platforms.