Traditional embedding models force a compromise between performance and price. You either pay for high-accuracy models or you settle for faster, cheaper models that miss critical semantic nuances. The Voyage 4 series changes this dynamic by introducing shared embedding spaces, the first industry capability that allows different models to work within a common multi-dimensional space. This means you can finally optimize your document storage and your query latency independently, creating a RAG architecture that is as cost-effective as it is precise.
In this technical webinar, we explore:
Asymmetric retrieval: Learn how to search document embeddings generated by a flagship model using a lightweight query model—without any re-processing.
Mixture of Experts (MoE): Discover how the new voyage-4-large uses MoE architecture to deliver state-of-the-art accuracy at 40% lower cost than dense models.
Multi-scale precision: See how matryoshka learning and quantization allow you to shrink embeddings from 2048 down to 256 dimensions with minimal quality loss.
End-to-end evaluation: Review the data from 29 datasets in the Retrieval Embedding Benchmark (RTEB) to see how these optimizations perform in the real world.
Join Apoorva Joshi, Senior AI Developer Advocate at MongoDB, for an educational session designed for architects and engineers who need to scale AI without breaking the bank.
