embed-english-v3.0 API cost is typically usage-based, and in most pricing schemes you pay based on the number of tokens you embed (and sometimes other request-shape constraints). That means your monthly bill is driven by: (1) how many documents you embed during ingestion, (2) how often you re-embed updated content, and (3) how many queries you embed at runtime. The most reliable way to answer “how much does it cost” is to compute it from your own traffic shape: average tokens per chunk × chunks per document × documents per month, plus average tokens per query × queries per month.
From an engineering budgeting perspective, don’t stop at the embedding API line item. Your total cost includes storing and searching vectors. If you store embeddings in a vector database such as Milvus or Zilliz Cloud, your storage cost scales with the number of vectors you create (chunking multiplies vector count) and the vector dimension (embed-english-v3.0 produces 1024-dimensional vectors). Your query cost is influenced by index configuration and top-k. Many teams find that embedding cost is predictable, while the retrieval stack cost can vary depending on how aggressively they tune for low latency and high recall.
A practical method to estimate cost quickly is a pilot run. Take a representative slice of your corpus (say, 10k chunks), embed it, record total tokens embedded, and measure how many vectors you generated per document. Then extrapolate. Do the same for queries: log token counts for a day of real queries, then project to monthly volume. Once you have those numbers, you can choose chunk sizes and overlap policies that meet retrieval quality targets without exploding token volume. This “measure first” workflow is more accurate than relying on any single posted rate, because pricing and limits can differ by provider, plan, and access channel.
For more resources, click here: https://zilliz.com/ai-models/embed-english-v3.0