Scaling Vector Database Operations with MongoDB and Voyage AI 2025

The performance and scalability of your AI application depend on efficient vector storage and retrieval. In this webinar, we explore how MongoDB Atlas Vector Search and Voyage AI embeddings optimize these aspects through quantization—a technique that reduces the precision of vector embeddings (e.g., float32 to int8) to decrease storage costs and improve query performance while managing accuracy trade-offs.

Vector embeddings are the foundation of AI-driven applications, along with powerful capabilities such as retrieval-augmented generation (RAG), semantic search, and agent-based workflows. However, as data volumes grow, the cost and complexity of storing and querying high-dimensional vectors increase.

Join Senior Staff Developer Advocate Anant Srivastava to learn how quantization improves vector search efficiency. We’ll cover practical strategies for converting embeddings to lower-bit representations, balancing performance with accuracy. In a step-by-step tutorial, you'll see how to apply these optimizations using Voyage AI embeddings to reduce both query latency and infrastructure costs.