Yes, jina-embeddings-v2-base-en is fast enough for many real-time Retrieval-Augmented Generation systems, provided it is deployed with sensible performance practices. Although it is larger than lightweight embedding models, it still delivers query-time embeddings quickly enough to fit within interactive latency budgets for most applications. In typical RAG systems, embedding the query is only one part of the total response time.
In a standard real-time pipeline, the query is first embedded, then sent to a vector database such as Milvus or Zilliz Cloud for similarity search. These databases are optimized for low-latency nearest-neighbor retrieval, even with large datasets. When embedding and search are both tuned properly, overall latency remains acceptable for chat-based interfaces, search UIs, and internal tools.
To maintain performance, developers often batch embedding requests, cache frequent queries, and generate document embeddings offline rather than at request time. Monitoring p50 and p95 latency across the entire pipeline is important, as bottlenecks may appear outside the model itself. For most English RAG use cases, jina-embeddings-v2-base-en provides a practical balance of semantic quality and speed when combined with Milvus or Zilliz Cloud for fast vector retrieval.
For more information, click here: https://zilliz.com/ai-models/jina-embeddings-v2-base-en