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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.

ସୂତ୍ର

Embedding Cost = (Total Tokens / 1,000) × Price per 1K Tokens
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

ଷ୍ଟେପ୍-ଷ୍ଟେପ୍ ଗାଇଡ୍ |

  1. 1Enter the total number of documents or text chunks to embed
  2. 2Specify the average token count per chunk (or paste sample text for auto-estimation)
  3. 3Select the embedding model and its per-token pricing
  4. 4View total cost for initial embedding plus estimated monthly re-embedding costs

ସମାଧାନ ହୋଇଥିବା ଉଦାହରଣ

ଇନପୁଟ୍
100,000 documents, avg 500 tokens each, using OpenAI text-embedding-3-small ($0.02/1M tokens)
ଫଳ
Total tokens: 50M. Cost = 50 × $0.02 = $1.00 for the entire corpus. Re-embedding 5% monthly: $0.05/month.
ଇନପୁଟ୍
1M documents, avg 800 tokens, using text-embedding-3-large ($0.13/1M tokens)
ଫଳ
Total tokens: 800M. Cost = 800 × $0.13 = $104.00. Significant savings vs. ada-002 at $0.10/1K tokens.

ଏଡ଼ାଇବା ଯୋଗ୍ୟ ସାଧାରଣ ଭୁଲ

  • ✕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

ବାରମ୍ବାର ଜିଜ୍ଞାସା

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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