Edge AI model families in 2026 prioritize small size, low latency, and privacy-preserving deployment patterns for on‑device and near‑edge inference. This topic examines those compact vision and multimodal architectures (quantized transformers, distilled ViTs/CNNs, and split‑execution hybrids) and compares “edge‑first” offerings such as Bonsai AI with enterprise alternatives. The market is shaped by three converging trends: tighter privacy and governance requirements that favor on‑device processing; advances in model compression and quantization that make small foundation models practical at the edge; and an expansion of no‑code/low‑code platforms that speed integration into business workflows. Key platform roles: Mistral AI provides enterprise‑oriented, efficiency‑focused foundation models and a production platform emphasizing privacy and governance for customers that need customizable, secure models. Cohere supplies private, customizable LLMs, embeddings and retrieval services that can support near‑edge inference patterns and cloud‑assisted workflows. No‑code/low‑code vendors (Anakin.ai, StackAI, Relevance AI) lower the operational barrier to deploy and orchestrate agents and vision pipelines on edge devices or in edge clusters. DatologyAI addresses a core upstream need—curated, model‑ready training data to make compact models faster and smaller without sacrificing accuracy. Evaluations should therefore consider model family tradeoffs (accuracy vs. compute/energy), deployment pathways (fully on‑device, split‑inference, or near‑edge), and ecosystem services (governance, data curation, orchestration). For practitioners, the practical choice hinges on device constraints, privacy/regulatory requirements, and whether the organization prioritizes turnkey no‑code deployment or bespoke, production‑grade model customization.