Generative AI applications need consistent and high-performance inference. This is crucial during unexpected traffic spikes. For example, chatbots may handle busy holiday sales. Fine-tuned models may support real-time analytics. Performance issues can hurt user experience and business results. This chapter discusses provisional throughput in Amazon Bedrock. This feature helps you plan. It allows you to pre-allocate compute resources. This ensures reliability. It also provides low latency. You will learn about model units (MUs). You will also discover best practices for scaling generative AI workloads. Cost considerations and real-world examples will be covered too. By the end, you'll understand how to optimize generative AI performance while balancing scalability and expenses effectively.