Embedding Cost Calculator
Calculate the costs of generating embeddings and storing vectors in a vector database.
Usage Parameters
Cost Breakdown
💰 Cost Optimization Tips
- Use smaller embedding models (e.g., text-embedding-3-small) for general use cases
- Batch your embedding requests to reduce API calls
- Consider self-hosted vector databases for large-scale deployments
- Implement caching to reduce redundant embedding generation
- Monitor and optimize your vector dimensions based on your accuracy requirements
Estimating embedding and vector storage costs
Embeddings turn text into numeric vectors that capture meaning, which is what powers semantic search and retrieval-augmented generation (RAG). Building a RAG system has two recurring costs: generating embeddings for your documents (and for every incoming query), and storing those vectors in a vector database such as Pinecone, Weaviate, Qdrant, or Milvus. Both scale with the size of your corpus and your query volume, and they're easy to underestimate.
This calculator estimates the one-time cost of embedding your documents plus the ongoing cost of vector storage and query embeddings, so you can size a RAG project before committing to it. It's especially useful for comparing a small, cheap embedding model against a larger one, and for seeing how chunking strategy affects the total vector count.
How to use this tool
- Enter the number of documents and their average length to estimate total embedding tokens.
- Choose an embedding model and add your expected monthly query volume.
- Review the split between one-time indexing cost and recurring storage and query costs.
Frequently asked questions
How is embedding pricing different from chat pricing?+
Embedding models only process input — there's no generated output — so they're billed per input token at a much lower rate than chat models. The recurring cost usually comes from re-embedding queries and from vector storage, not from the initial indexing.
What drives vector storage cost?+
The number of vectors (chunks) and their dimensionality. Smaller chunks improve retrieval precision but multiply the vector count, so chunking strategy directly affects both quality and storage cost.