Vector Database Architecture Calculator
Calculate if standard PostgreSQL pgvector can handle your AI/RAG workload, or if you need to migrate to a dedicated Vector Database (Pinecone, Qdrant) based on memory index limits.
AI/RAG Workload Inputs
The total corpus of raw text, PDFs, or products you plan to embed.
How many overlapping chunks you split each document into for embedding (usually 5-10).
The output size of your embedding model (e.g. 768 for small models, 1536 for OpenAI).
Float32 is standard. Halfvec (Float16) cuts RAM in half with negligible recall loss.
Index & Memory Configuration
Sizing Verdict
Verdict: Green — Standard pgvector is perfectly sufficient. A dedicated vector database is overkill.
Total Vectors
0
Raw Storage
0.0 GB
Estimated Minimum RAM (For Index)
0.0 GB RAM
HNSW indexes must fit entirely in RAM to maintain fast query latency. If this exceeds your instance capacity, queries will hit disk and slow down drastically.
Estimated Total Disk Storage (Includes Indexes, Metadata, WAL)
~0 GB
Nuance
20M vectors is not a hard pgvector limit; it is a practical "benchmark before committing" threshold. HNSW has better query performance than IVFFlat but slower build times and much higher memory usage. Move to dedicated vector DBs when p95 latencies exceed your SLO.