text-embedding-ada-002 is considered inexpensive relative to the value it provides, especially for general-purpose semantic tasks. Its pricing is low enough that developers can embed large volumes of text without significant cost, making it suitable for both experiments and production systems. This affordability has contributed to its widespread adoption.
In practical terms, cost depends on how much text you embed and how often embeddings are generated. Many applications embed documents once and reuse the vectors for a long time, which keeps ongoing costs minimal. Query-time embedding usually involves short text inputs, further reducing expense. This makes the model predictable and easy to budget for.
Infrastructure choices also affect overall cost. By storing embeddings in a vector database such as Milvus or Zilliz Cloud, developers can avoid repeatedly recomputing embeddings and focus spending on efficient storage and search. This architecture keeps both compute and operational costs under control while supporting scalable semantic features. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002