Embedding Cost Calculator
Paste your text to count tokens and calculate the exact cost to generate embeddings across all major providers.
(scale cost for your corpus)
Tokens (per doc): 0
Words: 0
Characters: 0
Cost (per doc): $0.000000
Total corpus cost: $0.000000
All Model Comparison (this text)
| Model | Tokens | Rate (per 1M) | Cost (per doc) | Cost (×1 docs) |
|---|
Embedding Cost Calculator — All Major Providers
Text embeddings convert text into numerical vectors that capture semantic meaning. They power semantic search, recommendation systems, clustering, and retrieval-augmented generation (RAG). This calculator shows you exactly how much it costs to embed your text with each provider.
Choosing an Embedding Model
| Model | Dimensions | Price/1M | Best For |
|---|---|---|---|
| text-embedding-3-small | 1,536 | $0.02 | Cost-efficient RAG |
| text-embedding-3-large | 3,072 | $0.13 | High-precision similarity |
| Voyage AI voyage-3 | 1,024 | $0.008 | Cheapest high-quality option |
| Cohere embed-v3 | 1,024 | $0.10 | Multilingual & classification |
| Google text-embedding-004 | 768 | $0.025 | Gemini ecosystem |
Tips to Reduce Embedding Costs
- Chunk documents optimally (512–1,024 tokens) to avoid re-embedding overlap.
- Cache embeddings — only re-embed when content changes.
- Use
text-embedding-3-smalloverada-002— it's cheaper and more accurate. - Voyage AI offers the cheapest high-quality embeddings at $0.008 / 1M tokens.
Related Tools
- Vector Database Cost Estimator — estimate storage and query costs after embedding
- RAG Pipeline Cost Calculator — full RAG cost including embedding, vector DB, and LLM inference
- Fine-Tuning Cost Calculator — compare embedding+RAG vs fine-tuning costs
- Token Cost Converter — convert token counts to USD for any model