Pinterest's discovery engine relies heavily on embedding retrieval, powered by their in-house platform Manas. Manas supports billions of embeddings across various product surfaces, but faces scaling challenges with memory costs and model expressiveness. To address this, they are evolving the retrieval stack through three key initiatives. First, they are employing quantization techniques like Scalar Quantization and Product Quantization to significantly reduce embedding index memory footprints and serving costs. Second, they are implementing SSD-based serving, specifically using SPANN with PQ quantization, to move indices off expensive RAM while maintaining low latency. This approach dramatically reduces memory usage and CPU costs compared to in-memory solutions. Third, Manas is transitioning from restrictive two-tower models to multi-embedding retrieval, allowing for richer scoring functions and more nuanced similarity assessments. This shift enables late interaction models, moving beyond the single-vector representation of candidates. Future plans include further optimizing costs with SPANN and SPFresh, enhancing multi-embedding support, and exploring GPU-based retrieval systems. These advancements aim to create a scalable, cost-efficient, and innovative embedding retrieval architecture for Pinterest.