Embedding Cost Calculator

Token count drives embedding costs more than model choice. A 10,000-document knowledge base with 500 tokens per document costs the same as a 5,000-document base with 1,000 tokens per document — both use 5 million tokens total. The surprise factor is chunking strategy: splitting long documents into smaller chunks can actually increase costs if you create more total tokens through overlap.

This calculator assumes you will embed each document once upfront, plus any new documents added monthly. Most embedding projects follow this pattern — an initial bulk embedding job to populate your vector database, followed by incremental updates. The tool uses current API pricing from major providers, which can change as the market evolves.

Embedding costs scale linearly with token volume, but vector storage and search costs scale differently. A million embeddings might cost $20 to generate but $200/month to store and query in a managed vector database. Factor in the complete pipeline cost, not just the embedding generation, when budgeting your semantic search project.

The choice between providers often comes down to model quality versus cost trade-offs. OpenAI's small model handles most general-purpose tasks at the lowest price point. Their large model costs significantly more but excels at nuanced semantic understanding. Cohere's models offer competitive quality with different pricing structures, particularly for multilingual content.

Use this calculator before starting any semantic search or RAG (retrieval-augmented generation) project. Embedding costs can surprise developers who estimate based on document count rather than token volume. A seemingly small project with 1,000 PDFs can cost hundreds of dollars if each PDF contains 10,000+ tokens.

Calculate costs early when choosing between embedding providers. The price difference between OpenAI's small and large models becomes significant above 10 million tokens. For a 50-million-token project, the large model costs $6.50 versus $1.00 for the small model — but the quality difference might justify the extra cost for critical applications.

Run cost projections for different chunking strategies before implementation. Embedding full documents versus smart chunking can change costs by an order of magnitude. Test your chunking approach with a small sample, measure token usage, then project costs before processing your full dataset.

The biggest cost mistake is embedding without chunking strategy. Developers often embed entire documents, hitting token limits and paying for unused content. A 5,000-word article might contain 6,500 tokens, but only 1,000 tokens actually matter for search. Smart chunking by paragraph or section can cut embedding costs by 60-80% while improving search relevance.

Another common error is re-embedding unchanged content during updates. If you update 10% of a document, only that section needs new embeddings — not the entire document. Implement change detection to avoid unnecessary re-embedding costs, especially with frequently updated content like news articles or product descriptions.

Overlooking vector storage costs leads to budget surprises. Generating embeddings is a one-time cost, but storing and searching millions of vectors costs significantly more over time. A 100MB embedding file might cost $0.50 to generate but $50/month to host in a managed vector database. Plan for the complete infrastructure cost, not just the embedding generation.

Embedding costs follow a simple formula: (total tokens ÷ 1,000,000) × cost per million tokens. For example, 50,000 documents at 400 tokens each equals 20 million tokens. With OpenAI's text-embedding-3-small at $0.02 per million tokens, the cost is (20 ÷ 1) × $0.02 = $0.40 total.

Token counting varies by provider but generally follows the same patterns. English text averages 4 characters per token, so a 1,600-character document typically uses 400 tokens. Technical content with specialized terminology may use more tokens per character, while simple text uses fewer. Always test your specific content type with the provider's token counting tools.

Monthly ongoing costs add up quickly at scale. If you embed 5,000 new documents monthly at 500 tokens each (2.5 million tokens), that costs (2.5 × $0.02) = $0.05 per month with the small model. Over a year, the ongoing cost ($0.60) might exceed your initial embedding cost, making model efficiency increasingly important for high-volume applications.

Batch processing can reduce costs through provider discounts, but the token calculation remains the same. Some providers offer volume pricing above certain thresholds — typically starting around 100 million tokens monthly. At that scale, negotiated rates can reduce costs by 10-30% compared to standard API pricing.

Token overhead from chunking overlap is invisible but expensive. Most production systems use 10-20% overlap between chunks to avoid splitting sentences, but this overlap counts as additional tokens. A 1,000-document corpus with 500 tokens per document and 15% overlap costs 15% more than expected — turning a $1 job into $1.15.