Embedding Cost Calculator
Calculate monthly and annual costs of generating text embeddings with OpenAI or Cohere models.
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How to use this calculator
- 1
Enter the number of texts you need to embed each month (documents, product descriptions, messages, etc.).
- 2
Enter the average number of tokens per text. A typical paragraph is 100–300 tokens.
- 3
Select your embedding model and compare costs instantly.
Frequently asked questions
What is an embedding?
An embedding converts text into a high-dimensional numeric vector that captures semantic meaning. Similar texts have vectors that are close together in space. Embeddings power semantic search, recommendation engines, and RAG pipelines.
Which embedding model should I use?
text-embedding-3-small is the most cost-efficient and performs well for most tasks. text-embedding-3-large offers higher accuracy for complex retrieval tasks at 6× the cost. ada-002 is the legacy model — 3-small outperforms it at lower cost.
Do I pay to embed documents multiple times?
Only if you re-embed them. The typical pattern is to embed each document once and store the vector. You only pay again if documents are updated or you change models.
Embedding Cost Calculator — OpenAI & Cohere Pricing
What drives embedding costs
Embedding costs scale with total token volume: the number of texts multiplied by their average length. Longer documents cost more to embed but often don't need to be split as finely. Chunking strategy — how you split documents before embedding — directly affects your monthly bill.
Caching embeddings to cut costs
If you embed the same texts repeatedly (e.g., a fixed product catalog queried by many users), cache the vectors in a vector database. You pay once to generate the embedding and retrieve it for free on every query. This can reduce embedding spend by 90%+ for read-heavy workloads.
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Results are estimates for informational purposes only and do not constitute professional financial, medical, legal, or technical advice. Read full disclaimer →