You batch requests with text-embedding-3-small by sending multiple text inputs in a single embedding call, reducing overhead and improving throughput. Batching is especially important when processing large datasets, such as embedding entire document collections or historical logs.
Instead of embedding each text item individually, developers typically group texts into batches based on size or count limits. This reduces repeated setup costs per request and allows the embedding service to process data more efficiently. For example, when embedding thousands of documentation paragraphs, batching them into groups of 50 or 100 texts can significantly improve overall throughput. The optimal batch size depends on input length, latency requirements, and system limits, so it’s worth testing under realistic conditions.
Batching pairs naturally with vector databases like Milvus and Zilliz Cloud. After generating a batch of embeddings, you can insert them into the database in bulk, which is far more efficient than single-row inserts. Bulk insertion reduces indexing overhead and speeds up ingestion pipelines. For large-scale systems, this approach keeps embedding generation, storage, and indexing aligned, resulting in faster data onboarding and more predictable performance.
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