Liquid AI is a startup incubated at MIT CSAIL, focused on developing edge-native AI solutions based on liquid neural networks, providing full-stack technology from base models to applications.
Its core advantages lie in adopting a non-Transformer liquid neural network architecture to deliver efficient, low-latency AI inference on-device, while offering stronger explainability and a smaller resource footprint.
Liquid AI models are optimized for edge devices and can run directly in resource-constrained environments such as smartphones, in-vehicle systems, and IoT devices, without relying on cloud servers.
Liquid AI offers some open-source models (such as LFM2) and a free mobile app (Apollo); enterprise-grade custom solutions require contacting the business team for a specific quote.
All AI inferences are performed locally on-device; data does not need to be uploaded to the cloud, fundamentally protecting user privacy and security, especially suitable for sensitive industries like finance and healthcare.
The main difference lies in deployment: Liquid AI emphasizes on-device, local operation to reduce network latency and cloud dependency; traditional services rely on cloud servers for data processing and computation.
Developers can rapidly integrate and deploy small language models on Android, iOS, and other systems via its LEAP platform and Edge SDK; official documentation and open-source model support are provided.
Dify AI is an open-source intelligent agent workflow-building platform that enables you to rapidly create and deploy AI applications for real-world business scenarios by visually composing LLMs, tools, and data sources with low-code, drag-and-drop workflows. It lowers the barrier to AI app development, supporting the full lifecycle from prototype to production deployment.
Lightly Vision AI is a computer vision–focused intelligent data management and model training platform designed to boost AI development efficiency and model performance by improving data quality. It provides end-to-end tools—from data selection and annotation to model training and edge deployment—helping machine learning teams handle large-scale vision data more efficiently.
