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Scaling vector search is one of the biggest challenges for AI-driven applications. We tested MongoDB Atlas Vector Search with a 15.3 million vector dataset and found it retains 90-95% accuracy with less than 50ms of query latency. In our new blog post, we break down the methodology, key performance factors like quantization and dimensionality, and what the results mean for your own large-scale workloads. Learn how to optimize your deployment. Read the full analysis: https://mongodb.social/6186fQ
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The Embedding and Reranking API on MongoDB Atlas is officially in Public Preview! VoyageAI by MongoDB frontier embedding and reranking models, available as a simple standalone API. Use it with any stack, or combine it with Atlas Vector Search. What you get: • Access the new Voyage 4 series for better context and reduced hallucinations. • Your data, vector search, and models all under one control plane. • Simple token-based pricing 200M free tokens to start building. Build end-to-end AI retrieval
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