AI inference is evolving from experimental setups to production environments, leading to a split in infrastructure options. On one side, reserved compute platforms, such as SF Compute and Modal, prioritize predictability and control. Customers pay for guaranteed access to GPUs with stable performance and clear economics, making these platforms suitable for workloads requiring consistency. However, this model limits growth to customers willing to manage their own infrastructure. On the other side, inference APIs, similar to services from Stripe and Twilio, focus on absorbing complexity and utilization risk. They offer elastic capacity and operational simplicity but at the cost of some control. Customers buy tokens or requests rather than hardware, benefiting from built-in scaling and batching. This approach allows for higher overall utilization through customer aggregation, making it appealing for bursty workloads, even if individual performance may be less predictable. The core tradeoff between these two models is clear: reserved compute excels in making individual customer performance predictable, while inference APIs thrive by efficiently serving many customers simultaneously. As AI workloads diversify, both markets remain viable, catering to different priorities. Companies that manage GPU resources directly can optimize utilization more effectively, which often outweighs the benefits of systems-level performance improvements. For most inference scenarios, reducing idle GPU time is the key to lowering costs, rather than simply increasing throughput.