The main limitations of text-embedding-3-large are higher resource usage, reliance on preprocessing quality, and the fact that embeddings are an approximate semantic signal. While the model captures meaning well, it does not guarantee perfect retrieval or reasoning, especially in edge cases.
Higher dimensionality means increased storage and memory usage compared to smaller embeddings. This can affect indexing time and query latency if not managed properly. Additionally, embeddings alone cannot enforce business rules, permissions, or exact matches for identifiers like version numbers or error codes. Poor chunking can also dilute meaning, causing irrelevant results even with a strong model.
These limitations are best addressed at the system level. Using a vector database such as Milvus or Zilliz Cloud allows developers to combine semantic similarity with metadata filters and structured constraints. Embeddings should be treated as one signal in a broader retrieval system. When designed carefully, the limitations are manageable and predictable, but they are important to understand before deploying text-embedding-3-large in production.
For more information, click here： https://zilliz.com/ai-models/text-embedding-3-large