Embedding Cost Calculator
Calculate the cost of AI embedding models for your RAG applications.
Estimate monthly cost for scaling your RAG application
Total Cost
$0.0000
Embedding Model Comparison
Cost for 0 tokens
| Provider | Model | Dimensions | Price / 1M | Cost |
|---|---|---|---|---|
| OpenAI | text-embedding-3-small Most cost-effective embedding model from OpenAI | 1536 | $0.02 | $0.0000 |
| OpenAI | text-embedding-3-large Highest quality embeddings with 3072 dimensions | 3072 | $0.13 | $0.0000 |
| OpenAI | text-embedding-ada-002 Legacy Ada model, still widely used | 1536 | $0.10 | $0.0000 |
| Cohere | embed-english-v3.0 Best for English text embeddings | 1024 | $0.10 | $0.0000 |
| Cohere | embed-multilingual-v3.0 Supports 100+ languages | 1024 | $0.10 | $0.0000 |
| Mistral | mistral-embed Mistral embedding model for RAG applications | 1024 | $0.10 | $0.0000 |
How It Works
Embedding models convert text into vector representations used for semantic search and RAG. Cost is calculated by multiplying your total token count by the model's price per million tokens. Our token estimation provides a close approximation of actual token counts.
RAG Cost Factors
- Text volume:Total document tokens
- Re-indexing:Costs recur per update
- Query embedding:Small per-query cost
- Dimension size:Affects storage cost
Frequently Asked Questions
What is an embedding model?
An embedding model converts text into numerical vectors that capture semantic meaning. These vectors are used in search, recommendation systems, and RAG applications to find relevant content based on meaning rather than exact keywords.
How is embedding cost calculated?
Embedding cost is calculated based on the number of tokens processed. Each provider charges a rate per 1 million tokens, which is multiplied by your total token count to get the final cost.
Which embedding model is most cost-effective?
OpenAI text-embedding-3-small is currently the most cost-effective at $0.02 per 1M tokens, making it ideal for high-volume applications. For higher quality embeddings, the 3-large model at $0.13 per 1M tokens offers better accuracy.
Why are embeddings important for RAG?
Embeddings are the core of RAG systems. They enable semantic search over your documents, allowing the AI to find and use relevant information. Accurate embedding cost estimation is critical for production RAG budgets.