Scale should make the model cheaper

AI product teams face difficult cost forecasting when renting models through per-token APIs, as inference expenses scale directly with usage and can reduce software gross margins compared with traditional software economics. Agentic workloads compound this uncertainty by consuming vastly more tokens than standard chats and varying significantly between runs. Running open model weights internally can offer more predictable fixed compute costs and enable durable optimizations such as quantization, distillation, custom routing, batching, and caching, while insulating users from vendor price changes. However, self-hosting adds operational engineering responsibilities and can be inefficient when demand is low or highly variable, making rented APIs more suitable for early-stage or unpredictable workloads. Although model inference prices have fallen rapidly and many firms improve unit economics through routing and management techniques alone, the choice depends largely on utilization, traffic patterns, and whether fixed infrastructure costs can outperform growing token bills.

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