This topic covers the architecture and providers that enable confidential AI “factories”: secure, auditable execution environments for open LLM (OLLM) or AI-local (AILO) workloads that must protect code, models, and data. It focuses on cloud platform integrations and hypervisor integrations that pair hardware-backed confidential computing (TEEs, secure enclaves, hypervisor isolation) with runtime sandboxes, memory/metadata services, and standard protocols for LLM-to-infrastructure coordination. Relevance in 2025: regulatory requirements, IP protection, and wider OLLM adoption have made confidential execution and minimal-trust integrations operational priorities. Organizations are moving beyond basic VM isolation to composable stacks that combine sandboxed execution for AI-generated code, deterministic memory services for context, and Model Context Protocol (MCP) bindings to connect models to external systems without leaking sensitive state. Key tools and roles: - Daytona: elastic, isolated sandboxes for running AI-generated code securely, minimizing blast radius between runs. - YepCode MCP Server: an MCP-compatible server that turns LLM-generated scripts into sandboxed, manageable processes for AI tooling. - Cloudflare MCP servers: deploy and interrogate developer platform resources (Workers, KV, R2, D1) via MCP endpoints to bridge models and cloud services. - mcp-memory-service: a production-ready hybrid memory store offering fast local reads, cloud sync, zero-lock semantics, and semantic memory search for assistant context. Taken together, these components form practical patterns for confidential AI pipelines: hardware or hypervisor-backed isolation, sandboxed runtime execution for untrusted model outputs, standardized MCP bindings for safe integrations, and hybrid memory services to preserve context without centralizing sensitive data.