ScaleDown targets AI inference costs with task-specific small models

Neal Patel launched ScaleDown, a service offering task-specific small language models designed to replace larger frontier models for simpler AI workloads. Patel claims the models are 15x cheaper, 63x faster, and 5.1% more accurate than GPT 5.4 Mini, though these figures come from his own launch post rather than an independent benchmark. The startup targets the estimated 70% to 80% of AI tasks that do not require a frontier model, aiming to significantly reduce inference costs for businesses.