text-embedding-3-large scales well for large datasets when paired with a vector database designed for high-dimensional similarity search. While the model produces higher-dimensional embeddings, modern vector databases are built to handle this efficiently through approximate nearest neighbor indexing and distributed storage.
In practice, scaling involves two stages: ingestion and querying. During ingestion, embeddings are generated—often in batches—and inserted into the database. Batch processing is critical to maintain throughput and control costs. For querying, approximate indexes allow similarity search to remain fast even as datasets grow to millions or tens of millions of vectors. The slight increase in per-query computation due to higher dimensionality is usually manageable with proper index configuration.
Vector databases like Milvus and managed services such as Zilliz Cloud handle horizontal scaling, sharding, and memory management. This allows developers to focus on application logic instead of infrastructure tuning. In real systems, factors like chunking strategy, metadata filtering, and index parameters often have a larger impact on scalability than the embedding model itself. With thoughtful design, text-embedding-3-large can support large-scale production workloads reliably.
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