voyage-2 is designed primarily for developers and technical teams building applications that rely on semantic understanding of text. This includes backend engineers, search engineers, and platform teams who need reliable embeddings as part of a larger system. It is not aimed at end users directly; instead, it serves as an infrastructure component that other services depend on. If your application needs to answer “Which pieces of text are most relevant?” voyage-2 is part of the solution.
The model is especially suitable for teams building semantic search, internal knowledge bases, customer support tools, or retrieval-augmented generation systems. For example, a developer working on an internal Q&A system can use voyage-2 to embed policy documents and employee questions. A data engineering team might use it to cluster or deduplicate large text datasets. In these scenarios, voyage-2 provides consistent embeddings that can be reused across multiple features and services.
voyage-2 is also a good fit for teams already using or planning to use a vector database such as Milvus or Zilliz Cloud. These users benefit most because they can immediately operationalize the embeddings at scale. The combination is well-suited for production environments where reliability, performance, and maintainability matter. In short, voyage-2 is designed for developers who want a practical, production-ready way to represent text as vectors and build meaningful retrieval systems on top of them.
For more information, click here: https://zilliz.com/ai-models/voyage-2