Event Concluded
Data Management
Scaling Vector Database Operations with MongoDB and Voyage AI
By MongoDB

Overall Rating
Share
About Event
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 such as retrieval-augmented generation (RAG), semantic search, and agent-based workflows. However, as data volumes grow, the costs and complexities of storing and querying high-dimensional vectors increase. Join Staff Developer Advocate Richmond Alake 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.
Tags:
Big DataVector Database Scaling MongoDB Vector Search Voyage AI Integration AI-Powered Database Operations Vector Data Processing MongoDB AI Workshop Scalable Vector Databases
Related Content on Data Management
Related News on Data Management
Trending Events & Webinars
Javascript & Angular Days Berlin
Mon, Oct 20, 2025
By Entwickler.De Akademie
Microsoft Fabric Community Conference 2025
Sat, Mar 29, 2025
By The Microsoft Fabric Community Conference
Courage XL 2025
Mon, Mar 17, 2025
By Courage XL
SAP Insider Las Vegas 2025
Tue, Mar 18, 2025
By SAPinsider
Dutch Php Conference 2025
Tue, Mar 18, 2025
By Dutch PHP Conference
Join Our Newsletter
Get weekly news, engaging articles, and career tips-all free!
By subscribing to our newsletter, you're cool with our terms and conditions and agree to our Privacy Policy.
Join The Discussion