Embedding Cost Calculator

Calculate embedding API costs and storage requirements across OpenAI, Cohere, and self-hosted models.

Number of documents

Avg tokens per document

Embedding model

Vector dimensions

Batch updates per month

Tab to move between fields

Initial embedding cost ($)

$1.00

Annual embedding cost ($)

$3.40

Monthly update cost ($)$0.20

Total tokens (millions)50

Vector storage (MB)292.97

Cost per 1K docs ($)$0.02

Vector count50,000

Effective dimensions1,536

Default Dims1,536

Total Storage GB0.29

How to Use This Calculator

Enter the Number of Documents (or text chunks) you need to embed — e.g., 50,000 for a mid-size knowledge base.
Set the average Tokens per Document (a typical page is ~500 tokens) and select the Embedding Model.
Review Initial Embedding Cost for the one-time indexing expense and Vector Storage in MB to size your database.
Enter Batch Updates per Month to account for re-embedding as your corpus changes.
Check Annual Embedding Cost and Cost per 1K Docs to compare self-hosted vs. API-based embedding strategies.

How the result changes with Number of documents

Number of documents: Total documents or text chunks to embed.
Avg tokens per document: Average token count per document. A typical page is ~500 tokens.
Embedding model: Select the embedding model
Vector dimensions: Output vector dimensions. Affects storage size. 3-small: 1536, 3-large: 3072, ada-002: 1536, Cohere: 1024.
Batch updates per month: Monthly re-embedding batches for corpus updates (~10% of docs per batch).

Initial embedding cost ($): One-time cost to embed your full document corpus.
Monthly update cost ($): Cost for monthly re-embedding of updated documents.
Annual embedding cost ($): Initial embedding + 12 months of updates.
Total tokens (millions): Total tokens in the document corpus.
Vector storage (MB): Storage required for all vectors in float32 format.
Cost per 1K docs ($): Embedding cost per 1,000 documents.
Vector count: Total number of embedding vectors.
Effective dimensions: Dimensions per vector (may differ by model).

Identify Input Parameters
4 parameters
Number of documents = 50000, Avg tokens per document = 1000, Embedding model = 0, Vector dimensions = 1536 = 5 input(s) provided
Calculate Initial embedding cost
Initial embedding cost
1 = $1
Calculate Annual embedding cost
Annual embedding cost = initialCost + monthlyUpdateCost * 12
3.4 = $3.4
Calculate Monthly update cost
Monthly update cost = updateCost * batchUpdatesPerMonth
0.2 = $0.2
Calculate Total tokens
Total tokens = totalTokens / 1_000_000
50 = 50