AI Embedding Cost Calculator
Calculate vector database Embedding generation and storage costs, comparing OpenAI, Cohere, Jina and other mainstream models
Calculator Interface
Interactive calculator will be available soon
Features
- ✓ Supports OpenAI text-embedding-3, Cohere embed-v3, Jina, Voyage and other mainstream models
- ✓ Accurately calculate Embedding generation costs for millions of documents
- ✓ Estimate vector database storage costs (Pinecone, Weaviate, Qdrant, Milvus)
- ✓ Compare cost-effectiveness across dimensions and models
- ✓ Provides cost optimization suggestions: caching, quantization, tiered storage
How to Use
- Select Embedding model (supports multi-model comparison)
- Input document count and average document length (tokens)
- Choose vector database and storage period
- View total cost analysis including generation + storage + query fees
FAQ
What is AI Embedding Cost Calculator?
An online AI Embedding Cost Calculator. It Calculate vector database Embedding generation and storage costs, comparing OpenAI, Cohere, Jina and other mainstream models. Input: the data or requirements you need processed. Output: AI-generated results with one-click copy and download. Everything runs in your browser — no uploads.
What are the main components of Embedding costs?
Three main parts: generation cost (per-token billing), storage cost (vector DB billing by GB or vector count), query cost (per-read billing).
Which Embedding model has the best cost-performance ratio?
Overall, OpenAI text-embedding-3-small offers the best value ($0.02/million tokens), suitable for large-scale scenarios; Cohere embed-v3 is recommended for high-precision use cases.
How to estimate vector database storage costs?
Depends on vector dimensions, document count, and index type. For example, 1536 dimensions with 1M documents uses ~6GB storage; Pinecone ~$7/month, self-hosted Milvus is cheaper.
How to reduce Embedding costs?
Use caching to avoid regeneration, choose lower-dimensional models, quantize historical data (FP16/INT8), tier hot/cold data storage.
Does it support RAG scenario cost estimation?
Yes. Input daily query volume and average context length; the tool estimates monthly RAG total cost (Embedding + vector retrieval + LLM generation).