Artificial intelligence deployment is increasingly balancing between Edge AI and Cloud AI to optimize latency, privacy, and scalability in various industries. Edge AI focuses on running AI models locally on devices near the data source, enabling real-time decision-making, protecting sensitive data, and reducing bandwidth consumption, which is crucial for applications like autonomous vehicles and industrial robotics. On the other hand, Cloud AI utilizes centralized servers for large-scale training and inference, offering elastic compute resources and simplified management, making it ideal for extensive data analytics and global model updates. A hybrid approach, combining both paradigms, often provides the best solution, allowing enterprises to train models in the cloud and deploy them at the edge, thus achieving a balance between performance, costs, and compliance. Emerging trends such as 5G, tiny models, and federated learning, along with solutions like Clarifai's compute orchestration, are enhancing AI deployment by simplifying model management across diverse environments. The future of AI lies in hybrid architectures that leverage both edge and cloud capabilities, ensuring that enterprises can create efficient, secure, and scalable AI systems.