Small language models: Rethinking enterprise AI architecture

Small language models (SLMs) are gaining traction in enterprise AI as a cost-effective, faster, and more private alternative to large language models for narrow, repetitive tasks. Typically under 10 billion parameters, SLMs are built using techniques like knowledge distillation, pruning, and quantization. They excel at classification, document processing, chatbots, and edge/IoT scenarios where low latency and data privacy matter. Gartner predicts enterprise use of task-specific SLMs will be three times that of LLMs by 2027. However, SLMs are not LLM replacements — the recommended approach is a routing architecture that sends simple queries to SLMs and complex ones to LLMs, orchestrating multiple models across different deployment contexts.