SLMs vs. LLMs: When Smaller Wins

There is a reflex in AI engineering right now: when in doubt, reach for the biggest model you can afford. GPT-4o for the customer bot. Claude Opus for the internal search tool. A frontier-class model for the document classifier that runs ten thousand times a day. That reflex is expensive. And in a growing number of production scenarios, it is also wrong. Small language models are no longer a compromise you accept when you cannot afford the real thing. They are a deliberate architectural choice that, in the right context, beats larger models on latency, cost, privacy, and even accuracy.