RAG Cost Estimator

Calculate the total cost of your Retrieval Augmented Generation (RAG) system, including vector embeddings, storage, and LLM API calls. Compare providers like OpenAI and Pinecone to optimize your deployment costs.

Document Processing

Retrieval Settings

Provider Selection

OpenAI Ada 2 Embeddings

$0.0001 / 1K tokens

OpenAI Text Embedding V3

$0.00002 / 1K tokens

Pinecone Vector Storage

Starting at $0.0035 per 1000 vectors/month

Monthly Cost Breakdown

Embedding Generation: $0.00
Vector Storage: $0.00
Query Costs: $0.00
Total Monthly Cost: $0.00

💡 RAG Cost Optimization Tips

  • Optimize chunk size to balance retrieval quality and storage costs
  • Use efficient embedding models to reduce API costs
  • Implement caching for frequently accessed vectors
  • Consider batch processing for document updates

Understanding RAG Cost Estimation

Key Features

  • Complete RAG Cost Analysis Calculate all components of your RAG system including embeddings, storage, and API calls in one place
  • Multi-Provider Support Compare pricing across major providers like OpenAI and Pinecone to find the most cost-effective solution
  • Real-time Calculations See instant cost updates as you adjust parameters, helping you optimize your configuration
  • Detailed Cost Breakdown Understand exactly where your costs come from with itemized calculations for each component

Practical Applications

  • System Architecture Planning Make informed decisions about your RAG system design based on cost implications
  • Budget Forecasting Project monthly costs and plan your budget with confidence using real provider pricing
  • Scaling Analysis Understand how costs scale with document count, query volume, and chunk size
  • Provider Evaluation Compare different providers and configurations to find the optimal setup for your needs

Cost Components Explained

  • Embedding Generation $0.00002-0.0001/1K tokens The cost of converting your documents and queries into vector representations
  • Vector Storage $0.0035/1K vectors Monthly costs for storing your document vectors in a database like Pinecone
  • Query Processing Costs associated with similarity search and retrieving relevant chunks
  • API Integration Additional costs for LLM API calls when generating responses with retrieved context

📈 Cost Optimization Strategy

Start with smaller chunk sizes (256-512 tokens) and fewer retrieved chunks (2-3) per query. Monitor retrieval quality and gradually adjust these parameters to find the optimal balance between cost and performance for your specific use case.