What drives vector database cost the most?
Primary cost drivers are chunk count, vector dimension, replication strategy, and query throughput. Overly aggressive chunking and retention policies can rapidly inflate monthly spend.
RAG Vector DB Cost Calculator
Estimate chunk count, embedding storage, vector index size, and monthly database cost for your RAG knowledge base.
Knowledge Base Configuration
Document Corpus
Chunking Strategy
0% (no overlap)50%
Embedding Model
1536 dims · float32 = 6,144 bytes/vector · $0.02/1M tokens
Vector Database & Query Load
Serverless pay-per-use. Queries billed as read units (vectors scanned × top-k).
Configure your corpus and click Calculate
Results will appear here
RAG Storage Tips
- Chunk size 256–512 tokens works well for most enterprise documents. Larger chunks reduce chunk count and storage but hurt precision retrieval.
- Overlap at 10–20% improves recall for boundary-straddling concepts without ballooning storage significantly.
- Smaller embedding dims (768d vs 3072d) can cut storage 4× with minimal quality loss for domain-specific corpora — consider fine-tuning before scaling up dims.
- Self-hosted vector DBs (pgvector, Chroma) are cheapest at scale but require ops overhead. Use managed services (Pinecone, Qdrant Cloud) for fast time-to-production.
- Re-ingestion cost is one-time. The main recurring cost is storage + query load. Cache frequent retrievals to reduce query billing.
How to use RAG Vector DB Cost Calculator for AI Architects
1. What this calculator does
Projects storage, index, and query cost for RAG infrastructure as corpus volume and retrieval traffic grow, helping teams prevent silent infrastructure cost drift.
2. When to use it
- Before selecting a managed or self-hosted vector stack.
- When retrieval corpus growth is outpacing budget expectations.
- During architecture reviews for long-term RAG operating cost.
3. Inputs explained
- Document and chunk volume, including expected growth rate.
- Embedding dimension and retention/replication settings.
- Read/write query load and retrieval fan-out assumptions.
- Provider pricing model for storage, throughput, and operations.
4. Formula / decision logic
- Vector count and index size are estimated from chunk policy and corpus scale.
- Monthly cost includes storage footprint plus query throughput charges.
- Scenario modeling compares baseline and growth-period spend.
- Decision output flags when architecture changes are needed to stay within budget.
5. Example scenario
A documentation platform expands from product docs to internal runbooks and ticket history. Vector growth doubles monthly spend. The calculator identifies chunk-policy adjustments and retrieval filtering as the fastest path to cost stabilization.
6. Architecture implications
- RAG cost planning should include both ingestion and retrieval lifecycle phases.
- Chunking and reranking decisions materially alter vector infrastructure footprint.
- Cost governance requires telemetry on index growth and query intensity.
- Storage optimization and retrieval quality must be balanced, not optimized independently.
7. Common mistakes
- Planning with static corpus size assumptions.
- Ignoring query amplification from broad top-k retrieval defaults.
- Treating vector store selection as purely feature-driven without cost benchmarking.
- Not recalculating costs after chunking policy changes.
8. Related calculators
9. FAQ
How does chunking policy affect vector DB spend?
Smaller chunks and high overlap increase vector count, index size, and write/read load. Chunking strategy should be tuned jointly with retrieval quality goals.
Should we optimize storage first or query path first?
For many workloads, query path optimization (top-k tuning, filtering, reranking strategy) reduces both cost and latency faster than storage-only optimizations.