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 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).