LLM Calculators

Embeddings Cost Calculator

Estimate the cost of embedding your document corpus.

Calculate the cost of creating vector embeddings for semantic search, RAG pipelines, and document retrieval. Compare pricing across OpenAI text-embedding-3-small, text-embedding-3-large, Cohere embed, and Voyage models.

10,000
500

Total tokens to embed: 5,000,000

ModelProviderPrice/1MOne-Time CostDimensions
text-embedding-3-smallOpenAI$0.0200$0.10001,536
text-embedding-3-largeOpenAI$0.1300$0.65003,072
Cohere embed-v3Cohere$0.1000$0.50001,024
Voyage 3Voyage AI$0.0600$0.30001,024

Understanding Embedding Costs

Embedding models convert text into numerical vectors used for semantic search, RAG (Retrieval-Augmented Generation), and similarity matching. The cost depends on your total token count and the embedding model chosen. OpenAI text-embedding-3-small costs $0.02 per million tokens — one of the most affordable options. text-embedding-3-large costs $0.13 per million tokens with higher dimensional output for better accuracy.

Planning for Recurring Embedding Costs

Initial embedding is a one-time cost, but most production systems re-embed documents periodically as content changes. If your corpus changes daily, you may need to re-embed updated documents each day. A 100,000-document corpus at an average of 500 tokens per document costs approximately $1.00 for a full re-embed with text-embedding-3-small. Factor in re-embedding frequency when budgeting.

Frequently Asked Questions

How much does it cost to embed 1 million documents?
Assuming an average of 500 tokens per document (500M total tokens), embedding 1 million documents costs approximately $10 with OpenAI text-embedding-3-small ($0.02/M tokens), $65 with text-embedding-3-large ($0.13/M), or $5-$50 depending on other providers. These are one-time costs unless you need to re-embed.
Which embedding model should I use for RAG?
For most RAG applications, OpenAI text-embedding-3-small offers the best cost-to-quality ratio at $0.02 per million tokens. Use text-embedding-3-large ($0.13/M) when retrieval accuracy is critical. Cohere embed-v3 and Voyage models offer competitive alternatives with different accuracy-cost tradeoffs.
How many tokens does a typical document have?
A typical document averages 300-800 tokens depending on type. A short email is about 100-200 tokens. A knowledge base article is 500-1,000 tokens. A full PDF page is roughly 500-700 tokens. Use our Token Calculator to get exact estimates for your specific content.

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Embedding pricing from official provider documentation. Storage costs for vector databases are not included in these estimates.