Modern embedded analytics layers is shifting from static dashboards to AI-driven interaction inside Saas products. As teams embed conversational capabilities into their analytics, they must decide between small and large language models. The SLM vs. LLM choice affects latency, token costs, governance, and deployment flexibility. Small models often handle frequent analytics queries efficiently, while large models support deeper reasoning. Many organizations adopt hybrid architectures that combine both. Platforms like Reveal allow teams to add AI to their analytics layer without sacrificing cost predictability, governance, or deployment flexibility.