What it is: Vector quantization in Atlas Vector Search enables more efficient vector storage and ingestion with automatic, built-in processing. Developers can choose scalar or binary quantization with rescoring, reducing memory usage by up to 96% while maintaining high retrieval accuracy. Who is it for: These vector quantization capabilities are built for teams scaling AI, semantic search, and retrieval-augmented generation (RAG) workloads. This capability combines MongoDB’s flexible document model with optimized vector compression, delivering lower cost, faster performance, and easier scalability for advanced use cases. Why it matters: By compressing vector data with minimal accuracy loss, vector quantization makes search and AI workloads more cost-efficient and performant. Running natively in Atlas eliminates the need for external preprocessing pipelines, helping developers simplify deployments and scale confidently across environments. How to get started: Click the documentation link below for setup guidance, configuration options, and best practices for vector quantization usage in Atlas Vector Search.