You can reduce costs when using text-embedding-3-large by optimizing how often you generate embeddings and how many vectors you store. The model itself is efficient for its size, but cost control mostly comes from system-level decisions rather than the embedding call alone.
One effective strategy is to avoid re-embedding unchanged content. For example, if documents are versioned, you can store a hash of the text and only regenerate embeddings when the content changes. Another common approach is careful chunking: splitting documents into too many small chunks increases the number of embeddings and storage cost, while overly large chunks reduce retrieval quality. Finding a balanced chunk size reduces both embedding and storage costs without sacrificing usefulness.
On the storage side, vector databases like Milvus and Zilliz Cloud allow you to use metadata filtering to limit search scope, which reduces query cost. You can also archive or delete embeddings for outdated content. In production systems, many teams use text-embedding-3-large only for collections where high accuracy is critical, and rely on smaller embeddings elsewhere. This selective use keeps overall costs under control while preserving quality where it matters most.
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