Artificial intelligence is rapidly advancing across industries, offering competitive advantages but also escalating infrastructure costs, with AI budgets expected to rise by 36% annually. Inference workloads now dominate AI spending, constituting 65% of AI compute budgets, and inefficiencies such as idle resources and misconfigured storage erode profitability. Optimizing AI infrastructure costs has become crucial, requiring a comprehensive approach across compute orchestration, model lifecycle management, data pipelines, inference engines, and FinOps governance. Tools like Clarifai's Compute Orchestration and Reasoning Engine are highlighted for their ability to efficiently manage resources and reduce costs through dynamic scaling and model optimization. The guide emphasizes the importance of AI-native orchestration, lifecycle management, data pipeline optimization, intelligent inference scaling, and adopting FinOps practices to manage costs effectively. Emerging trends such as API price compression, specialized silicon, and green computing are also shaping the landscape of AI cost optimization. By leveraging these strategies and tools, organizations can transform AI from a cost center into a strategic advantage.