This topic examines the practical differences between Meta’s Limitless smart‑glasses platform and the broader ecosystem of AR/AI device SDKs, with emphasis on Edge AI vision platforms and AI tool marketplaces. In 2025 the field is defined by two converging trends: increasingly capable multimodal models (for vision, speech and text) and a shift toward split‑compute architectures that balance on‑device inference, low-latency pipelines, and cloud‑based model services. Key components in this landscape include multimodal model APIs (Google Gemini) and cloud ML backends (Google Vertex AI) for training, hosting and monitoring models; no‑code marketplaces and app libraries (Anakin.ai) that accelerate app assembly and repetitive workflows; low‑latency voice/agent platforms (Vogent) for conversational agents and TTS; enterprise assistant frameworks (IBM watsonx Assistant) for orchestrating multi‑agent flows; and web‑grounded answer engines (Perplexity AI) for sourcing real‑time information. Meta’s Limitless sits alongside these tools as a device‑centric platform that must integrate on‑device sensors, AR rendering, and remote model inference via SDKs. For developers and product teams the tradeoffs are familiar: on‑device models improve privacy and responsiveness but increase engineering complexity and power constraints; cloud models offer scale and frequent tuning but add latency and connectivity dependencies. Marketplaces and no‑code platforms reduce integration overhead, while specialized SDKs for voice, vision, and retrieval remain essential for production wearables. Evaluating platforms today requires looking beyond raw model capability to SDK support for sensor fusion, provisioning and update workflows, latency budgets, privacy controls, and the availability of prebuilt components in AI marketplaces. This comparison helps teams choose the right mix of edge inference, cloud services, and developer tooling for real‑world smart‑glasses applications.