Free AI Architecture Tool
Enterprise RAG and Vector Database Sizing Calculator.
Estimate total semantic chunks, HNSW vector index memory, and the right retrieval architecture (PostgreSQL pgvector vs. distributed Qdrant/Milvus, hybrid BM25 + cross-encoder reranking, and on-premises GPU vs. private cloud hosting) from your document volume and daily query load.
1. Enter Corpus Size & Query Load
2. Vector Index & Architecture Sizing
150,000 Vector Chunks
- Total Pages
- 60,000
- Est. HNSW Index RAM
- 1,172 MB
Recommended Retrieval Stack: PostgreSQL (pgvector HNSW) + Hybrid Search (BM25 + Vector) + Cross-Encoder Reranker
On-Premises: Single enterprise GPU server running vLLM + local multilingual embedding/reranker models.
How We Size Production Retrieval-Augmented Generation (RAG) Systems
Most enterprise corpora under a few hundred thousand chunks do not need a complex distributed vector cluster: PostgreSQL with pgvector HNSW indexing, combined with BM25 lexical search and a cross-encoder reranker, delivers high recall with minimal operational overhead.
For air-gapped or strict data-residency environments, ALGOFACT deploys local multilingual embedding, reranking, and vLLM inference servers under ISO/IEC 27001:2022 with document-level access control.
Frequently Asked Questions
01When is PostgreSQL (pgvector) better than a standalone vector database?
Up to roughly 250,000–500,000 chunks, keeping vectors and document permissions inside PostgreSQL avoids dual-write synchronization bugs and reduces infrastructure complexity.
02Why include a cross-encoder reranker in the RAG pipeline?
Bi-encoder vector search is fast at retrieving the top 30–50 candidates, while a cross-encoder reranker scores the exact query-passage pairs so the LLM only sees the top 5–8 highest-precision passages.
03Can ALGOFACT deploy the entire RAG stack on-premises without external API calls?
Yes. We deploy open-weight LLMs, embedding models, and vector stores directly on your on-premises GPU servers or private cloud VPC.
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