text-embedding-3-small is relatively inexpensive to use compared to larger embedding models, making it suitable for both experimentation and production workloads. Its design focuses on efficiency, which directly translates into lower per-request cost and lower infrastructure overhead. This makes it easier for teams to embed large datasets or process frequent queries without budget surprises.
Cost shows up in two main places: embedding generation and downstream storage or retrieval. Because text-embedding-3-small produces compact vectors and processes text quickly, the cost per embedded token is low. For example, embedding thousands of short documents or queries per day is typically affordable even for small teams. This is especially useful for applications like semantic search or recommendation systems where embeddings may be generated frequently.
Storage and query costs are also lower because of the smaller vector size. When embeddings are stored in a vector database such as Milvus or Zilliz Cloud, smaller dimensions mean reduced memory usage and faster indexing. Over time, this can significantly reduce operational costs. For developers, the takeaway is that text-embedding-3-small enables scalable semantic features without requiring enterprise-level budgets or aggressive cost optimization.
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