This topic covers AI model marketplaces and foundries focused on healthcare and robotics: platforms that host, validate, package, and distribute models, prebuilt agents, and deployment stacks for regulated, latency-sensitive applications. By 2026 the space has coalesced around three practical needs: curated, auditable training data; composable agent and model bundles that include governance metadata; and edge-first inference stacks for robotic and medical devices. Typical offerings include model hubs and marketplaces (e.g., Hugging Face) that provide model cards, fine-tuning and hosted inference; foundry-style bundles (e.g., HOPPR AI Foundry) that combine validated models, deployment templates, and compliance artifacts; and partner programs/accelerators (e.g., NVIDIA Inception) that optimize workloads for edge GPUs and vision sensors. Complementary tools and categories matter: data-curation platforms such as DatologyAI accelerate creation of model-ready datasets for clinical and robotics sensors; no-code/low-code agent platforms like StackAI and Lindy enable rapid assembly and governance of autonomous agents; Xilos offers observable agentic infrastructure for enterprise visibility and control; and enterprise assistant frameworks such as IBM watsonx Assistant integrate LLMs into multi-agent workflows. Foundational model providers (Google Gemini, Anthropic Claude family) and developer tools (GitHub Copilot) continue to supply backbone capabilities but must be adapted through fine-tuning, prompt engineering, and edge optimization. For healthcare and robotics, marketplaces and foundries are becoming essential not for generic model access alone but for delivering inspected, deployable artifacts—complete with data provenance, governance hooks, and edge deployment patterns—that meet performance, safety, and regulatory requirements.