AI Toolset

AI Embedding Cost Calculator

Estimate the total token count and API cost to embed a document corpus using OpenAI, Cohere, or Voyage embedding models.

How AI embedding costs are calculated

Embedding models convert text into numerical vectors used for semantic search and retrieval-augmented generation (RAG).

Providers charge per token, where one token is roughly 0.75 English words.

To estimate total cost, multiply your document count by average tokens per document to get total tokens, divide by 1,000,000, then multiply by the model's price per million tokens.

For example, embedding 100,000 documents averaging 500 tokens each produces 50 million tokens, costing about $1.00 with OpenAI's text-embedding-3-small at $0.020 per million.

Costs scale linearly with both document count and average document length, so trimming verbose content before embedding directly lowers your bill without hurting retrieval quality.

Reducing embedding costs for large document sets

Several techniques meaningfully reduce embedding costs without sacrificing retrieval quality.

First, chunk long documents into segments of 256 to 512 tokens rather than embedding entire pages — shorter chunks also improve retrieval precision because they match queries more closely.

Second, remove boilerplate text like headers, footers, legal disclaimers, and repeated navigation content before generating embeddings.

Third, deduplicate your corpus before ingestion, since identical or near-identical documents waste tokens with no retrieval benefit.

Finally, cache embeddings in a vector store and only re-embed documents that change rather than reprocessing the entire corpus on each update.

Combining these steps can cut embedding costs by 30 to 60 percent for typical enterprise document sets.

When to re-embed your corpus — and when not to

Re-embedding your entire corpus is expensive and often unnecessary.

You must re-embed when switching models because embeddings are model-specific and vectors from different models cannot be meaningfully compared — a vector from ada-002 is mathematically incompatible with one from text-embedding-3-small.

For content updates, use incremental embedding: delete the old vector for changed documents and embed only the revised version.

Avoid scheduling full re-embedding runs on a fixed interval if your content changes partially, as this burns budget on unchanged documents.

Plan model selection carefully before large-scale production ingestion, because upgrading mid-project requires a costly full corpus rebuild and downtime for the vector store migration.

Choosing the right embedding model for your RAG pipeline

Model selection balances cost, vector dimensions, and retrieval accuracy.

OpenAI's text-embedding-3-small is the lowest-cost option at $0.020 per million tokens and performs well for general English text, making it the default for most RAG applications. text-embedding-3-large offers higher accuracy for multilingual and technical content at $0.130 per million.

The older ada-002 is now largely superseded by the 3-series models.

Cohere and Voyage models offer competitive accuracy and are worth benchmarking if you handle multilingual corpora or have data residency requirements.

Run a retrieval benchmark on 100 to 200 representative queries before committing to a model for large-scale ingestion to avoid an expensive mid-project migration.