Many organizations significantly overspend on inference by failing to align their infrastructure with specific use case priorities. Experts suggest that reducing token production costs for non-real-time tasks is possible by prioritizing model quality and throughput over latency. This approach enables substantial efficiency gains for workloads like synthetic data generation and complex offline workflows.