Agentic systems and enterprise search depend on strong data retrieval that works efficiently and accurately. Database provider MongoDB thinks its newest embeddings models help solve falling retrieval quality as more AI systems go into production.
As agentic and RAG systems move into production, retrieval quality is emerging as a quiet failure point — one that can undermine accuracy, cost, and user trust even when models themselves perform well.
The company launched four new versions of its embeddings and reranking models. Voyage 4 will be available in four modes: voyage-4 embedding, voyage-4-large, voyage-4-lite, and voyage-4-nano.
MongoDB said the voyage-4 embedding serves as its general-purpose model; MongoDB considers Voyage-4-large its flagship model. Voyage-4-lite focuses on tasks requiring little latency and lower costs, and voyage-4-nano is intended for more local development and testing environments or for on-device data retrieval.
Voyage-4-nano is also MongoDB’s first open-weight model. All models are available via an API and on MongoDB’s Atlas platform.
The company said the models outperform similar models from Google and Cohere on the RTEB benchmark. Hugging Face’s RTEB benchmark puts Voyage 4 as the top embedding model.
“Embedding models are one of those invisible choices that can really make or break AI experiences,” Frank Liu, product manager at MongoDB, said in a briefing. “You get them wrong, your search results will feel pretty random and shallow, but if you get them right, your application suddenly feels like it understands your users and your data.”
He added that the goal of the Voyage 4 models is to improve the retrieval of real-world data, which often collapses once agentic and RAG pipelines go into production.
MongoDB also released a new multimodal embedding model, voyage-multimodal-3.5, that can handle documents that include text, images, and video. This model vectorizes the data and extracts semantic meaning from the tables, graphics, figures, and slides typically found in enterprise documents.
Enterprise’s embeddings problems
For enterprises, an agentic system is only as good as its ability to reliably retrieve the right information at the right time. This requirement becomes harder as workloads scale and context windows fragment.
Several model providers target that layer of agentic AI. Google’s Gemini Embedding model topped the embedding leaderboards, and Cohere launched its Embed 4 multimodal model, which processes documents more than 200 pages long. Mistral said its coding-embedding model, Codestral Embedding, outperforms Cohere, Google, and even MongoDB’s Voyage Code 3. MongoDB argues that benchmark performance alone doesn’t address the operational complexity enterprises face in production.
MongoDB said many clients have found that their data stacks cannot handle context-aware, retrieval-intensive workloads in production. The company said it’s seeing more fragmentation with enterprises having to stitch together different solutions to connect databases with a retrieval or reranking model. To help customers who don’t want fragmented solutions, the company is offering its models through a single data platform, Atlas.
MongoDB’s bet is that retrieval can’t be treated as a loose collection of best-of-breed components anymore. For enterprise agents to work reliably at scale, embeddings, reranking, and the data layer need to operate as a tightly integrated system rather than a stitched-together stack.
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