Voyage AI by MongoDB

You should not have to think about chunking. voyage-context-4 makes that possible. With auto-chunking, no 32K ceiling, and built-in overlap support, it delivers 2.1% better retrieval than voyage-context-3 at 33% lower cost. A strong fit for retrieval over long documents and agent context engineering. Learn more: mongodb.social/6013BDaj1v

🏆 VoyageAI just won the 2026 AI TechAward for Best Innovation in RAG Enablement! voyage-4-large delivers frontier-level retrieval accuracy (without the compute bill) thanks to an industry-first MoE architecture and shared embedding space. We’re incredibly proud of this team and we're just getting started! Learn more about voyage-4-large ➡️ mongodb.social/6012BBwXos

☁️ Voyage AI + Microsoft @Azure AI Foundry Voyage AI’s latest models are now available through Azure AI Foundry. • The Voyage 4 Advantage: Switch between text models without re-indexing thanks to our shared embedding space. • Enterprise Ready: Scalable deployment on Azure’s secure AI infrastructure. • Superior Accuracy: Access the flagship voyage-4-large for industry-leading retrieval quality. Get started with AI Foundry: ai.azure.com/catalog/publis…

The era of embedding models is evolving. 🚀 With voyage-4-large, we’ve moved to Mixture of Experts (MoE) to shatter the scaling ceiling. The results: ✅ Massive drop in inference cost and latency ✅ New frontier for retrieval accuracy. Curious about how we implement MoE embeddings? Read the full technical breakdown of how we optimized design choices to push the Pareto frontier: mongodb.social/6017h7EUh

The first 200 million tokens are free. Learn more in our release blog: mongodb.social/6017hmxNh

voyage-4-large sets a new accuracy standard, outperforming voyage-4, voyage-4-lite, Gemini Embedding 001, Cohere Embed v4, and OpenAI v3 Large by an average of 1.87%, 4.80%, 3.87%, 8.20%, and 14.05%, respectively.

📢 ICYMI: We recently released the Voyage 4 model family. Voyage 4 features an industry-first shared embedding space, eliminating the need to re-index your data when switching between models in the series. This breakthrough is designed to solve the trade-off between frontier-level accuracy and production efficiency.

Forget genres - what if you could search for a movie like this: “Something uplifting after a rough day” “A film that’ll make me cry” In this new tutorial with @huggingface, Arek Borucki takes you through how to build a mood-based recommendation engine using voyage-4-nano and MongoDB Atlas Vector Search. Give it a try! mongodb.social/6013hmoXl

Read the full guide: mongodb.social/6011h5mdf

🪆 Matryoshka Learning: Train embeddings such that early dimensions capture the most important info information. This allows you to truncate embeddings to reduce storage costs and speed up search while preserving semantic meaning. Voyage 4 supports 2048, 1024, 512, and 256 dimensions from a single model.

Building Gen AI prototypes is easy. Scaling them to production is hard. Our guide shows you how to optimize retrieval for accuracy, speed, AND cost. Covers asymmetric retrieval using Voyage 4, vector quantization, and Matryoshka Learning. Link below 👇

📢 Big news for @GoogleCloudTech developers! Voyage AI models have officially joined the Model Garden on Vertex AI. You can now access our frontier-level embeddings and capabilities in multiple GCP regions—including the industry-first shared embedding space from the Voyage 4 series—alongside Google’s native tools. SOTA Retrieval: Voyage models lead standard benchmarks with models optimized for accuracy and cost. Multimodal Excellence: Use voyage-multimodal-3.5 for unified search across text, images, and video. Easy Setup: Deploy directly from Vertex AI with just a few clicks. Find us in the Vertex AI Model Garden: bit.ly/3MmK649

🚀 Voyage AI models are on the @awscloud Marketplace! You can deploy Voyage 4 and voyage-multimodal-3.5 directly within your AWS VPC. Leverage your existing AWS credits and infrastructure to build high-performance RAG and agentic search systems. Full Data Privacy: Models run in your own account and VPC. Seamless Integration: Deploy as real-time inference endpoints via Amazon SageMaker. Unified Billing: One consolidated bill through your AWS account. Get started on AWS: mongodb.social/6019h6uYR

Stop choosing between accuracy and cost. The Voyage 4 model series offers unmatched price-performance flexibility for developers. Join Apoorva Joshi on Feb 19 at 9 AM PST to learn about: - Shared Embedding Spaces: Flagship accuracy with lite-model latency. - MoE Architecture: SOTA accuracy at 40% lower cost. - Matryoshka Learning and Quantization: Maintaining accuracy while slashing query latency Register here: mongodb.social/6010hGm62

🚀 Meet voyage-4-nano: Our first open-weight embedding model voyage-4-nano is ideal for local development and prototyping while providing an easy path to production. ✨ Shared Embedding Space: voyage-4-nano shares an embedding space as its larger siblings (voyage-4-large, voyage-4, and voyage-4-lite). This means you can use voyage-4-nano locally alongside our flagship models without re-indexing a single vector. 🔓 Open Weights: Freely available on @huggingface under the Apache 2.0 license. Download, run, and start building. 📏 Flexible & Efficient: Supports Matryoshka Representation Learning (MRL) and multiple quantization options (int8, binary). Get started today: mongodb.social/6011hy0ON

The Embedding and Reranking API on MongoDB Atlas is officially in Public Preview! @VoyageAI 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 in Atlas, from data storage and vector search to embeddings and reranking Read the full announcement: mongodb.social/6012hGFq0