The power of the cloud is a double-edged sword, offering limitless scalability but also the risk of huge cost due to inefficiency. This chapter provides a technical deep dive into optimization, offering a practical tips for making cloud and AI both powerful and economically sustainable. We begin by tackling cloud expenditures, revealing strategies to eliminate idle resources, master the high-risk, high-reward Spot Instances, and “right-size” computational workflows to match their true needs. We then move into the engine room of artificial intelligence, exploring parallelism techniques (data, tensor, and pipeline) required to train today’s massive models. Finally, we address the critical “last-mile” challenge of inference, where we unpack model compression and runtime strategies that transform computationally-heavy models into fast, efficient tools for real-world deployment.