About this Vector Database Cost Calculator
Vector databases often charge for stored embedding volume, query volume, or both. This calculator estimates those moving parts without requiring provider-specific pricing.
Who this calculator is for
- AI teams estimating RAG infrastructure cost before ingestion.
- SaaS builders comparing vector storage plans.
- Consultants preparing a cost model for document search or semantic retrieval.
When to use it
- Estimate monthly storage cost for embedding records.
- Add query pricing to see whether traffic or storage drives the bill.
- Compare embedding dimensions and metadata size before choosing a model.
How to use this calculator
- Enter the number of vectors you expect to store.
- Enter embedding dimensions, bytes per dimension, and metadata bytes per vector.
- Enter monthly storage and query prices from your vendor or estimate.
- Review storage GB, storage cost, query cost, and total monthly cost.
Key assumptions
- Pricing is user-entered because provider plans and free tiers change.
- The estimate does not include replicas, indexing overhead, backups, or minimum plan fees unless you include them in your rates.
- Storage uses binary GiB conversion with 1024^3 bytes per GB.
Dataset sources
- Effective date
- 2026-05-05
- Last reviewed
- 2026-05-05
Vector database billing can include minimums, pods, replicas, read units, write units, indexes, storage overhead, and discounts not modeled here.
Public vector database pricing references summarized as planning presets.
Accuracy and testing
Review formula sources, default test cases, assumptions, unit handling, rounding rules, and changelog for this calculator.
How this calculator works
Inputs explained
Vectors
The number of embedded chunks or records stored in the database.
Dimensions
The embedding length produced by your model, such as 768, 1,536, or 3,072 dimensions.
Bytes per dimension
Float32 values are commonly 4 bytes. Quantized vectors can be smaller.
Metadata bytes
Extra payload stored with each vector, such as source IDs, titles, tags, or URLs.
Understanding your results
Example scenarios
Small internal knowledge base
100,000 vectors at 1,536 dimensions and 4 bytes per dimension is about 0.59 GB before metadata. Query costs may matter more than storage for this size.
Key Features
- Instant Calculation
- Private & Secure
- Mobile Optimized
Tips & Best Practices
- Use the RAG chunk calculator first if you need an estimated vector count.
- Check whether your provider charges for replicas, pods, reserved capacity, or minimum monthly plans.
- Reducing dimensions or metadata size can lower storage, but may affect retrieval quality.