Rex says
Money math without the spreadsheet headache. Plug in your numbers and I'll show you exactly where the dollars land.
Try a scenario
Click to load — tweak from there.Inputs
Result
Estimated monthly cost
$16.48
Total storage/memory needed
47.1
Raw vector data (no overhead)
30.7
Annualized cost
$197.74
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How to use this
- 1Enter number of vectors.
- 2Enter embedding dimensions.
- 3Enter index overhead multiplier.
- 4Enter metadata per vector (bytes).
- 5Enter memory/storage rate ($/GB-mo).
- 6Read your estimated monthly cost on the right — it updates as you type.
- 7Hit Share to keep the scenario or send it to someone.
About this calculator
Vector database cost is driven by embedding dimensionality and count more than most teams expect, because each vector stores one 4-byte float per dimension plus index overhead, and popular embedding models range from 384 to 3072 dimensions — an 8x spread that directly multiplies your storage bill. This calculator computes raw vector storage (dimensions × 4 bytes × vector count), adds a metadata overhead estimate, applies an index overhead multiplier (HNSW-style indexes commonly add 1.2-2x overhead for graph structure on top of raw vector data), and multiplies by your storage or memory rate. Since most vector databases keep the index in memory (RAM) rather than on disk for query speed, the relevant cost is often a memory-backed instance price per GB rather than commodity block storage, which is why this tool's rate input defaults to a RAM-tier price rather than a cold-storage price.
Worked example
Using the values the calculator loads with:
Inputs
- Number of vectors: 5000000
- Embedding dimensions: 1536
- Index overhead multiplier: 1.5
- Metadata per vector: 200 bytes
- Memory/storage rate: 0.35 $/GB-mo
Results
- Estimated monthly cost: $16.48
- Total storage/memory needed: 47.1
- Raw vector data (no overhead): 30.7
- Annualized cost: $197.74
What each field means
Inputs
- Number of vectors
- The number of vectors used in the calculation. Starts at 5000000 so you have a working example on load.
- Embedding dimensions
- The embedding dimensions used in the calculation. Starts at 1536 so you have a working example on load.
- Index overhead multiplier
- The index overhead multiplier used in the calculation. Starts at 1.5 so you have a working example on load. Accepted range: 1–3.
- Metadata per vector (bytes)
- The metadata per vector used in the calculation, measured in bytes. Starts at 200 bytes so you have a working example on load.
- Memory/storage rate ($/GB-mo)
- The memory/storage rate used in the calculation, measured in $/GB-mo. Starts at 0.35 $/GB-mo so you have a working example on load.
Results
- Estimated monthly cost
- Returned as a money amount in US dollars and shown as the headline result. It recalculates instantly whenever you change an input, so you can compare scenarios without reloading.
- Total storage/memory needed
- Returned as a whole number. It recalculates instantly whenever you change an input, so you can compare scenarios without reloading.
- Raw vector data (no overhead)
- Returned as a whole number. It recalculates instantly whenever you change an input, so you can compare scenarios without reloading.
- Annualized cost
- Returned as a money amount in US dollars. It recalculates instantly whenever you change an input, so you can compare scenarios without reloading.
FAQ
Why does embedding dimension choice matter so much for cost?
Storage scales linearly with dimension count, so switching from a 3072-dimension embedding model to a 384-dimension one cuts raw vector storage 8x for the same vector count, and index memory cost falls proportionally. Unless your retrieval quality genuinely needs the higher-dimensional model, a smaller embedding model can cut vector database infrastructure cost dramatically with only a modest recall trade-off for many use cases.
Can I use quantization to reduce cost?
Yes — scalar or product quantization compresses each dimension from a 4-byte float to as little as 1 byte (int8) or less (binary quantization), cutting raw storage 4-32x, at the cost of some retrieval accuracy that's often recoverable with a re-ranking pass over quantized candidates. This is one of the highest-leverage cost optimizations for large vector databases and is worth testing before scaling out to more nodes.
Should the index stay entirely in memory?
For low-latency production search (sub-50ms), yes — most production vector databases (Pinecone, Weaviate, Milvus, pgvector with appropriate config) keep the HNSW graph in RAM because disk-based graph traversal is too slow for real-time queries. Disk-backed or hybrid approaches exist for cost-sensitive, latency-tolerant workloads but trade query speed for a lower memory bill.
Accuracy and limitations
- Results are estimates before tax, fees, and inflation unless an input explicitly covers them.
- Rates are treated as fixed for the whole period — variable-rate products will drift from this projection.
- This is educational maths, not financial advice. Check anything contractual with the lender or your accountant.
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Cite this calculator
Writing about this topic? Grab a citation — every link helps keep these tools free.
RevenueLab. (2026). Vector Database Storage Cost Calculator. Retrieved from https://www.revenuelab.fyi/toolbox/vector-db-storage-cost
<p>Source: <a href="https://www.revenuelab.fyi/toolbox/vector-db-storage-cost" target="_blank" rel="noopener">Vector Database Storage Cost Calculator — RevenueLab</a> (2026).</p>
Source: [Vector Database Storage Cost Calculator — RevenueLab](https://www.revenuelab.fyi/toolbox/vector-db-storage-cost) (2026).