voyage-2 balances cost and performance by aiming for embeddings that are “good enough” for high-quality retrieval without being excessively large or expensive to generate. From a performance standpoint, the model produces vectors with a fixed dimensionality that is suitable for fast similarity search. Smaller or moderate-sized vectors reduce memory usage, index size, and query latency when stored in a vector database. This directly affects infrastructure costs and response times.
On the cost side, voyage-2 is typically used in a way that minimizes repeated computation. Documents are embedded once and reused many times, which amortizes embedding costs over many queries. Developers also batch embedding requests during indexing to reduce overhead. Because query-time embedding usually involves only a single short text, its cost is small compared to the value it provides. This pattern—batch expensive work, keep online work lightweight—is central to how voyage-2 fits into production systems.
The balance becomes most visible when voyage-2 is paired with a vector database such as Milvus or Zilliz Cloud. Efficient indexing and approximate nearest neighbor search reduce the computational cost of retrieval, allowing systems to scale without linear increases in latency or expense. voyage-2 provides embeddings that work well with these indexing strategies, so developers don’t need to trade off relevance for speed manually. Instead, they get a predictable balance where embedding generation, storage, and search all stay within reasonable cost and performance bounds.
For more information, click here: https://zilliz.com/ai-models/voyage-2