Embedding Cost Calculator
Calculate costs for generating text embeddings with OpenAI, Cohere, and other providers
Embedding Model
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1 page ≈ 500-700 tokens
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Complete Guide to Text Embeddings and Vector Databases
What are Text Embeddings?
Text embeddings are numerical representations of text that capture semantic meaning in a way that computers can process. Instead of treating words as discrete symbols, embedding models convert text into dense vectors (arrays of numbers) where similar meanings are positioned closer together in the vector space.
For example, the phrases "How do I reset my password?" and "I forgot my login credentials" would have similar embedding vectors because they express related concepts, even though they use different words. This semantic understanding makes embeddings fundamental to modern search, recommendation systems, and RAG (Retrieval-Augmented Generation) applications.
Key Embedding Concepts
- Dimensions: The size of the vector (e.g., 1536 dimensions). More dimensions can capture more nuance but require more storage.
- Semantic Similarity: Embeddings enable measuring how similar two pieces of text are using cosine similarity or dot product.
- Context Length: Maximum tokens that can be embedded in a single request (varies by model).
- Vector Database: Specialized databases (Pinecone, Weaviate, Qdrant) optimized for storing and querying embeddings.
Understanding Embedding Costs
Embedding costs are calculated based on the number of tokens processed. Unlike LLM inference which has separate input/output pricing, embedding models charge a single rate per token. The formula is straightforward:
When planning embedding costs, consider both the initial embedding generation and any re-embedding needs when documents change. Many applications also need to embed user queries at search time, though query volumes are typically much lower than document volumes.
Embedding Model Comparison
| Model | Dimensions | Max Tokens | Price/M |
|---|---|---|---|
| text-embedding-3-small | 1,536 | 8,191 | $0.02 |
| text-embedding-3-large | 3,072 | 8,191 | $0.13 |
| text-embedding-ada-002 | 1,536 | 8,191 | $0.10 |
| Cohere embed-v3 | 1,024 | 512 | $0.10 |
| Voyage AI voyage-large-2 | 1,536 | 16,000 | $0.12 |
Common Use Cases and Cost Estimates
Knowledge Base (10K documents)
- Documents: 10,000
- Avg tokens/doc: 500
- Total tokens: 5,000,000
- Cost (3-small): ~$0.10
E-commerce Catalog (100K products)
- Products: 100,000
- Avg tokens/product: 200
- Total tokens: 20,000,000
- Cost (3-small): ~$0.40
Legal Documents (1K contracts)
- Documents: 1,000
- Avg tokens/doc: 5,000
- Total tokens: 5,000,000
- Cost (3-small): ~$0.10
Support Tickets (50K/month)
- Tickets: 50,000
- Avg tokens/ticket: 150
- Total tokens: 7,500,000
- Cost (3-small): ~$0.15/month
Storage Considerations
Beyond API costs, you need to consider vector storage. Each embedding dimension is typically stored as a 32-bit float (4 bytes). The storage formula is:
| Dimensions | 1K Vectors | 100K Vectors | 1M Vectors |
|---|---|---|---|
| 768 | 2.9 MB | 292 MB | 2.9 GB |
| 1,536 | 5.9 MB | 585 MB | 5.9 GB |
| 3,072 | 11.7 MB | 1.2 GB | 11.7 GB |
Best Practices for Cost Optimization
Optimization Strategies
- Choose the Right Model: text-embedding-3-small is 5x cheaper than ada-002 with comparable quality for most use cases.
- Batch Requests: Send multiple texts in a single API call to reduce overhead and potentially qualify for batch pricing.
- Preprocess Text: Remove unnecessary whitespace, boilerplate, and formatting to reduce token count.
- Cache Embeddings: Store embeddings to avoid re-computing for unchanged content.
- Use Dimensionality Reduction: Some models support truncating dimensions for reduced storage with minimal quality loss.
- Smart Chunking: Split documents intelligently at semantic boundaries rather than arbitrary character counts.
When to Use Different Models
Budget-Focused
Use text-embedding-3-small for general search, FAQs, and content where semantic matching is sufficient. Best for high-volume applications where cost is a primary concern.
Quality-Focused
Use text-embedding-3-large for complex semantic search, legal/medical content, or when subtle meaning differences matter. Worth the extra cost for precision-critical applications.
Code Search
Use specialized models like Voyage Code for code search and understanding. These models are trained on programming content and handle code syntax better than general-purpose models.
Important Considerations
- Model Consistency: Always use the same embedding model for documents and queries. Embeddings from different models are not compatible.
- Re-embedding Costs: Switching models or upgrading versions requires re-embedding all existing content.
- Rate Limits: Check provider rate limits - high-volume batch jobs may require planning around API quotas.