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Embedding Cost Estimator

The Embedding Cost Estimator projects the cost of converting documents, messages, products, or other records into vector embeddings. It uses record count, average tokens per record, refresh frequency, embedding token price, and optional processing charges.

This calculation is useful for retrieval-augmented generation, semantic search, recommendations, and clustering pipelines. It shows the token volume processed during initial indexing or repeated refreshes and helps distinguish one-time embedding expense from other storage and query costs.

Cost inputs

tokens
USD
USD
Result
—
Estimated cost
Tokens embedded—
Embedding token cost—
Cost per record—
Embedding runs—

1. Count embedding records

Use the number of chunks or items sent to the embedding model, not merely the number of source files.

2. Estimate average token length

Measure a representative sample after chunking and cleaning.

3. Set refresh frequency

Enter one for initial indexing or a higher value for repeated full re-embedding.

4. Apply token and processing rates

Enter the current embedding price and any separate pipeline expense.

5. Review volume and unit cost

Check total tokens, model cost, and effective cost per record.

Embedded tokens = Records × Tokens per record × Embedding runs Embedding token cost = Embedded tokens ÷ 1,000,000 × Token price Total cost = Embedding token cost + Other processing cost

Where:

  • Records — actual embedding inputs after chunking.
  • Embedding runs — number of complete passes during the period.
  • Other processing cost — optional ETL, parsing, or compute cost.

Assumptions: Every run processes all entered records at the same average length.

What the result means

The result estimates the cost of generating embeddings for the entered record volume and refresh schedule.

Vector storage and query charges are not included; use the Vector Database Cost Estimator for those components.

Given: 1,000,000 records, 350 tokens each, one embedding run, $0.10 per million tokens, and $25 processing cost.

Calculation: Embedded tokens = 1,000,000 × 350 = 350,000,000. Token cost = 350 × $0.10 = $35.

Result: Total estimated embedding cost = $60, or $0.000060 per record.

In this case, pipeline processing costs more than the embedding model itself.

Should I enter documents or chunks?

Enter the number of actual texts sent to the embedding endpoint. A document split into ten chunks counts as ten records.

How do updates affect cost?

Use a fractional or full-run equivalent based on the share of records re-embedded during the period.

Are query embeddings included?

Not unless you include them in the record count. Estimate query-time embeddings separately when their volume is meaningful.

Why use average tokens instead of characters?

Embedding services commonly bill by tokens, and token density varies by language and content type.

Does dimensionality change model cost?

Provider pricing may or may not depend on dimensions. The calculator follows the token rate you enter and does not add a dimension surcharge automatically.