Why are embeddings so cheap compared to LLM calls?

Embedding models are much smaller than generative LLMs and run a single forward pass per text (no token-by-token generation). OpenAI's text-embedding-3-small is 25x cheaper than GPT-4o mini for input processing. Embed everything once; query cheaply with vectors.

Which embedding model is best?

For English text: OpenAI text-embedding-3-large is reliable default. For quality: Voyage AI voyage-3 often benchmarks higher. For local/self-hosted: BGE-M3 and E5 families are strong open-source choices. For domain-specific: consider fine-tuned embeddings (Voyage offers law, code, finance variants).

How do I know how many embeddings I need?

Count documents × chunks per document. Typical chunking: 500-1000 tokens per chunk. A 1000-page corpus (~500k tokens) makes ~500-1000 chunks. Re-embedding when content updates, not from scratch, saves cost long-term — use content hashing to detect changes.

What's a good embedding dimension?

768-1536 is standard. Smaller (384) is faster and cheaper but slightly less accurate. Larger (3072+) is diminishing returns. Most production systems use 1024-1536. Storage cost matters at scale: 1M embeddings at 1536 dims = ~6GB in a vector DB.

AI & Prompt Tools · Free tool

Embedding Cost Estimator

Estimate total tokens and cost for embedding a corpus online. Compare OpenAI, Voyage, Cohere, and more at once — free tool, instant results.

Updated June 2026

Estimate how much it costs to embed a corpus into a vector database once. Re-embedding on every update multiplies the bill.

Number of documentsAvg tokens per document

Embedding model

Total tokens

50,000,000

One-off cost

$1.00

Prices are list rates as published by each vendor; volume discounts may apply. Query-side embedding cost is separate and usually much smaller.

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What it does

Estimate embedding cost for a corpus — compare OpenAI, Voyage, Cohere side by side.

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Paste this snippet into any page. Loads on-demand (lazy), no tracking scripts, and sized to most dashboards. Replace the height to fit your layout.

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How to use it

Enter document count and avg tokens.
Pick embedding models.
Read total cost per provider.

Frequently asked questions

Why are embeddings so cheap compared to LLM calls?: Embedding models are much smaller than generative LLMs and run a single forward pass per text (no token-by-token generation). OpenAI's text-embedding-3-small is 25x cheaper than GPT-4o mini for input processing. Embed everything once; query cheaply with vectors.
Which embedding model is best?: For English text: OpenAI text-embedding-3-large is reliable default. For quality: Voyage AI voyage-3 often benchmarks higher. For local/self-hosted: BGE-M3 and E5 families are strong open-source choices. For domain-specific: consider fine-tuned embeddings (Voyage offers law, code, finance variants).
How do I know how many embeddings I need?: Count documents × chunks per document. Typical chunking: 500-1000 tokens per chunk. A 1000-page corpus (~500k tokens) makes ~500-1000 chunks. Re-embedding when content updates, not from scratch, saves cost long-term — use content hashing to detect changes.
What's a good embedding dimension?: 768-1536 is standard. Smaller (384) is faster and cheaper but slightly less accurate. Larger (3072+) is diminishing returns. Most production systems use 1024-1536. Storage cost matters at scale: 1M embeddings at 1536 dims = ~6GB in a vector DB.

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