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RAG Vector Database & Embedding Storage Monthly Cost Estimator

Calculate monthly vector database bills, index RAM consumption, read/write query costs, and embedding storage across Pinecone, Supabase pgvector, Qdrant Cloud, and Cloudflare Vectorize.

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How to Use the Vector DB & RAG Cost Estimator

  1. STEP 01
    Enter your total indexed vector volume (e.g. 5,000,000 vectors) and your expected monthly vector query traffic (e.g. 12,000,000 searches).
  2. STEP 02
    Select your embedding model vector dimension (e.g., 1536 for OpenAI small or 3072 for large) and index algorithm (HNSW graph vs IVFFlat).
  3. STEP 03
    Optionally supply metadata payload size per vector (e.g. 256 bytes for chunk text, URL, and IDs).
  4. STEP 04
    Compare estimated monthly costs across Cloudflare Vectorize, Supabase pgvector, Pinecone Serverless, and Qdrant Cloud to discover the most cost-efficient architecture.

Frequently Asked Questions (Vector DB & RAG Cost Estimator)

How much RAM does vector indexing require in production?

Each float32 vector dimension consumes 4 bytes. For a 1536-dimensional vector, the raw vector data is ~6.14 KB. When using an HNSW (Hierarchical Navigable Small World) graph index for sub-10ms nearest neighbor recall, graph pointers add approximately 1.2× to 1.5× overhead, resulting in ~10 KB of RAM per indexed vector.

When should I choose Supabase pgvector over dedicated vector databases like Pinecone?

Supabase pgvector is ideal when your application already uses PostgreSQL for user accounts, billing, and transactional data. Storing vectors directly in Postgres allows you to perform relational JOINs, apply Row-Level Security (RLS), and run hybrid search (full-text search + vector similarity) in a single ACID query without dual-database synchronization lag.

How does Cloudflare Vectorize pricing work?

Cloudflare Vectorize charges based on queried vector dimensions ($0.014 per 1M dimensions queried) and stored vector dimensions ($0.05 per 1M dimensions stored per month), offering massive cost savings for serverless edge workers.