text-embedding-3-small is fast enough for many real-time applications, including interactive search, recommendation, and query matching. Its design emphasizes low latency and efficient computation, which makes it practical to generate embeddings on demand rather than precomputing everything in advance.
In a typical real-time scenario, such as semantic search, the system embeds the user’s query at request time and immediately performs a vector search. text-embedding-3-small can usually generate an embedding in milliseconds, depending on input length and infrastructure. This latency is small compared to overall request handling time and is often overshadowed by network or database query latency. For applications like chat search, internal tools, or developer dashboards, this performance is usually more than sufficient.
End-to-end speed also depends on the vector database. When embeddings are stored in Milvus or queried via Zilliz Cloud, similarity search is optimized for low latency using approximate nearest neighbor indexes. This means that even with millions of vectors, queries often return results within tens of milliseconds. Combined with fast embedding generation, text-embedding-3-small enables real-time user experiences without complex caching or precomputation strategies.
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