Estimate what it costs to embed your document or RAG corpus across OpenAI, Cohere, Google, and open-source models — plus the resulting vector storage footprint.
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Before a retrieval-augmented generation (RAG) system, semantic search index, or recommendation engine can do anything useful, every chunk of your corpus has to be converted into a numeric embedding vector — and that conversion is billed per token just like a chat completion. This embedding cost calculator takes the size of your corpus (number of chunks and average tokens per chunk) and computes the total cost to embed it across five common providers, from OpenAI's inexpensive text-embedding-3-small to Google's gemini-embedding and self-hosted open-source alternatives, alongside an estimate of how much disk space the resulting vectors will occupy.
The calculator multiplies your number of chunks by the average tokens per chunk to get total tokens processed, then applies the selected embedding model's price per 1 million tokens to compute total embedding cost, plus a normalized cost per 1,000 chunks so you can compare corpora of different sizes on equal footing. Separately, it estimates vector storage size by multiplying chunk count by embedding dimension by 4 bytes (since embeddings are typically stored as 32-bit floats), converting the result into megabytes and gigabytes. A bar chart compares total cost across all five models for the same corpus, and a second chart shows how storage size scales with embedding dimension.
Embedding cost is usually a one-time (or periodic, if your corpus updates) expense, but it can still add up meaningfully for corpora in the tens of millions of tokens, and the storage footprint has an ongoing cost through your vector database bill. Higher-dimension embeddings from premium models often improve retrieval quality, but the tradeoff is both a higher per-token embedding price and roughly proportionally larger storage — this calculator makes that tradeoff visible before you commit to a model and dimension for a corpus that may be expensive to re-embed later.
An embedding is a fixed-length list of numbers (a vector) that represents the meaning of a piece of text, so that semantically similar text produces vectors that are close together — the foundation of semantic search and RAG retrieval.
Higher-dimension embeddings (1536, 3072) can capture more semantic nuance but cost more to generate and store than smaller ones (384, 768). Many production RAG systems use 768-1536 dimensions as a practical middle ground.
Vector storage size scales directly with chunk count and dimension — doubling either one roughly doubles your storage footprint, which is why chunking strategy matters as much as model choice for cost planning.
Common questions about embedding costs
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