Designing modern data pipelines involves navigating a complex trade-off triangle among data latency, cost, and query speed, similar to the CAP theorem in distributed systems. Optimizing all three simultaneously is challenging, as enhancing one aspect often leads to compromises on the others. For instance, achieving low latency and fast queries typically results in high costs due to the need for advanced infrastructure, while optimizing for low cost and fast queries might sacrifice real-time data processing capabilities. Cube Cloud offers a flexible solution through its AI-powered universal semantic layer, enabling businesses to balance these trade-offs by choosing which two factors to prioritize according to their specific needs. Cube enhances query speed with advanced caching and indexing while controlling costs through intelligent data management, allowing companies to deliver rapid analytics without excessive expenses. Additionally, Cube supports lambda architecture to optimize costs by combining real-time and batch processing, providing fine-grained control for different parts of a data pipeline.