The growing prevalence of multimodal data streams—such as video, images, and text—poses new challenges for stream processing systems. Existing systems are designed for structured data and struggle to support multimodal workloads efficiently. While multimodal large language models (MLLMs) provide powerful semantic understanding, naively integrating them into streaming pipelines can incur excessive computation and degrade performance. This demonstration presents Samsara, a novel super-optimizer that enables real-time processing of multimodal data streams by aggressively specializing query plans to the query and data stream. Through an interactive traffic monitoring scenario, the demo shows how multimodal queries are optimized by transforming naive query plans into optimized ones, reducing redundant model invocations and improving responsiveness and execution accuracy.