Why I Switched From GPT-4 to Small Language Models for Two of My Products

GPT-4 and Claude Sonnet are not always the right model for the job. After 18 months of running AI products in production, I've moved two of my products from frontier models to small language models — and the results have been better latency, lower cost, and in one case, higher accuracy on the specific task. Here is exactly what I did and why. Background: The Two Products That Changed Product 1: AgriIntel — Crop recommendation classification AgriIntel uses AI to classify incoming sensor data events and route them to the appropriate recommendation workflow. The classification task is: Given a set of sensor readings (soil moisture, temperature, nutrient levels, weather forecast), classify what type of agronomic decision is needed: Irrigation Fertilization Pest management Harvest timing No action This is a classification task with a fixed taxonomy. GPT-4o was doing it well — but at $0.005 per classification , at 15,000+ classifications per day , the cost was significant. Latency was also 8