This topic covers architectures and toolchains for running conversational AI and code assistants where data residency, confidentiality, and auditability matter: private‑cloud hosting, on‑device inference, NEAR AI-style near‑edge deployments, and hybrid mixes that combine local models with cloud-based services. In 2025 enterprises are choosing data‑controlled AI to meet tighter regulation, reduce latency and egress risk, and retain operational control when public LLMs are inappropriate or unavailable. Key categories include decentralized AI infrastructure (private clusters, edge nodes, and orchestration), AI data platforms (RAG pipelines and document stores), security and governance (access controls, audit logs, model provenance), and regulatory compliance tooling (data residency, consent, and policy enforcement). Representative tools reflect these needs: Tabnine and JetBrains AI Assistant provide enterprise, in‑IDE code assistants with private/self‑hosted deployment and contextual controls; Cursor and Windsurf (formerly Codeium) embed AI across editor and agent workflows for developer productivity with on‑prem options; IBM watsonx Assistant focuses on enterprise virtual agents and multi‑agent orchestrations with no‑code and developer modes; MindStudio offers no‑/low‑code design and deployment for agent workflows with enterprise controls; LlamaIndex supplies developer primitives for RAG and document agents to keep retrieval and indexing inside controlled environments. Practical tradeoffs center on model size versus local resource limits, cost and complexity of private hosting, hybrid orchestration to fall back to cloud offerings, and operationalizing governance across model, data and deployment lifecycles. Organizations should evaluate tooling for self‑hosting support, policy and audit features, integration with document stores and RAG frameworks, and the ability to run inference on edge or device when connectivity or data sensitivity requires it.