Total Vector Chunks
30,000
Raw Vector Storage
175.0 MB
HNSW Graph Index RAM
218.8 MB
Recommended Hot RAM
512 MB
Vector Database Plan Sizing & Estimated Monthly Cost
| Vector Database Provider | Architecture | Calculated Storage | Recommended Plan | Estimated Monthly Cost |
|---|
Architecture Sizing Recommendation
Calculating database capacity…
Recommended PostgreSQL Vector Host
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Why Vector Database Sizing Stumps RAG Architects
In production Retrieval-Augmented Generation (RAG) pipelines, storage is deceptively more complex than standard tabular data. A common beginner misconception is assuming a vector database only needs enough disk space to store numbers. In reality, production vector databases have three distinct memory tiers:
- Raw Vector Floating-Point Data: An uncompressed 1,536-dimensional vector stored as 32-bit floats takes 6,144 bytes per chunk. 100,000 documents chunked into 600,000 vectors require 3.68 GB of raw vector storage alone.
- Hierarchical Navigable Small World (HNSW) Index Graph Overhead: Fast approximate nearest neighbor (ANN) retrieval requires pre-computed graphs connecting high-dimensional nodes. The HNSW graph requires between 1.1x and 1.35x additional RAM beyond the raw vectors.
- Document Metadata & Chunk Text Payload: The actual text chunks, page numbers, author IDs, and access-control lists (ACLs) stored alongside each vector occupy an additional 300 to 500 bytes per record.
Choosing Between pgvector, Qdrant, and Pinecone
When to Use PostgreSQL + pgvector
Unified relational data, strict ACID guarantees, and zero extra infrastructure:
- Under 1,000,000 Vectors: Native PostgreSQL HNSW indexing delivers sub-20ms queries while maintaining foreign keys to user tables.
- Complex SQL Filtering: Filter vectors alongside user permissions, tenant IDs, and creation timestamps in a single ACID query.
- Lowest Total Cost of Ownership: Deploy on managed platforms like Supabase for $0 to $25/month.
When to Use Dedicated Qdrant
Ultra-low latency, billion-scale datasets, or complex hybrid search:
- Over 1,000,000 Vectors: Native Rust architecture with on-disk payload storage and memory-mapped HNSW indexes.
- Quantization Support: Native scalar and binary quantization slashes RAM consumption by up to 97%.
- Sub-10ms Latency: Optimized for high-concurrency production search workloads.
Related Production Architecture Guides
- Building Production RAG with PostgreSQL and pgvector in Python — Step-by-step implementation of HNSW indexing, connection pooling, and hybrid search.
- AI Token & Cloud Inference Cost Estimator — Compare monthly LLM API bills vs. self-hosted vLLM GPUs.
- Production Turnkey AI Starter Kits — Download runnable code repositories with Redis caching, pgvector RAG, and FastMCP.
- Production AI Architectures Hub — Architectural diagrams and engineering specifications across 6 core AI blueprints.