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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.
공식
- 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
단계별 가이드
- 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
풀어진 예시
피해야 할 일반적인 실수
- ✕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
자주 묻는 질문
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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