How to Calculate LLM Embedding Cost

What is LLM Embedding Cost?

The LLM Embedding Cost Calculator estimates the total expense of generating vector embeddings for text data using models like OpenAI text-embedding-3-small, Cohere embed-v3, or open-source alternatives. It helps developers budget for RAG pipelines, semantic search, and recommendation systems.

Formula

Embedding Cost = (Total Tokens / 1,000) × Price per 1K Tokens

N: Number of Documents (documents) — Total text documents or chunks to embed
T_avg: Average Tokens per Document (tokens) — Mean token count per text chunk
P: Price per 1M Tokens ($/1M tokens) — Embedding model pricing rate
O: Overlap Ratio (0-0.5) — Fraction of overlapping tokens between consecutive chunks

Step-by-Step Guide

1Enter the total number of documents or text chunks to embed
2Specify the average token count per chunk (or paste sample text for auto-estimation)
3Select the embedding model and its per-token pricing
4View total cost for initial embedding plus estimated monthly re-embedding costs

Worked Examples

Input

100,000 documents, avg 500 tokens each, using OpenAI text-embedding-3-small ($0.02/1M tokens)

Result

Total tokens: 50M. Cost = 50 × $0.02 = $1.00 for the entire corpus. Re-embedding 5% monthly: $0.05/month.

Input

1M documents, avg 800 tokens, using text-embedding-3-large ($0.13/1M tokens)

Result

Total tokens: 800M. Cost = 800 × $0.13 = $104.00. Significant savings vs. ada-002 at $0.10/1K tokens.

Common Mistakes to Avoid

✕Confusing embedding model pricing (per million tokens) with LLM inference pricing (per thousand tokens) — embeddings are orders of magnitude cheaper
✕Not accounting for chunking strategy — overlapping chunks increase token count by 10-30%
✕Forgetting to budget for re-embedding when documents are updated or the model version changes

Frequently Asked Questions

Which embedding model is cheapest?

As of 2024, OpenAI text-embedding-3-small is one of the cheapest commercial options at $0.02 per million tokens, while offering strong performance. Open-source models like BGE, E5, or GTE are free to run but require GPU hosting costs. For most use cases under 10M tokens, commercial APIs are more cost-effective than self-hosting.

How many tokens is a typical document?

A standard 500-word document is approximately 600-700 tokens. For RAG applications, documents are typically chunked into 256-512 token segments with 50-100 token overlap. One token is roughly 4 characters or 0.75 words in English.

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