RAG Vector DB Cost Calculator
bps1418
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2026-06-27
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via Hacker News: Show HN
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.
9. FAQ
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.
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.
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