Vector Database Sizing Calculator
Estimate storage requirements, memory needs, and costs for your vector database deployment. Configure your embedding model, data volume, and performance requirements to get accurate sizing recommendations.
Input Mode
Calculate vectors from document size using chunking (recommended for RAG applications)
Vector Configuration
Latest OpenAI embedding model with highest performance
Number of dimensions in each vector
Precision affects storage size and performance
Index affects search performance and storage overhead
Data Volume & Growth
Total size of documents to be chunked and embedded
Tokens per chunk (256-512 is optimal for RAG)
Average bytes per token (4 for English text)
Additional metadata stored with each vector
Storage Requirements
Number of data copies for high availability
Indexes in RAM, vectors memory-mapped on disk
Percentage of vectors to keep in RAM cache
Maximum connections per node in HNSW graph
Reduce storage at cost of CPU overhead
Sizing Results
Configure your vector database to see sizing results