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Vector Database Cost Estimator

The Vector Database Cost Estimator models recurring vector-search infrastructure cost from stored vectors, dimensions, bytes per dimension, replicas, storage pricing, query volume, query pricing, and fixed platform fees. It provides a transparent baseline for managed services or an internal cost model.

The result estimates raw replicated vector storage and query charges separately. Metadata, indexes, minimum capacity, network transfer, backup, and compute can materially change a real invoice, so the fixed-fee field can be used for known charges that do not fit the simplified storage-and-query model.

Cost inputs

bytes
USD
USD
USD
Result
—
Estimated cost
Replicated vector storage—
Storage cost—
Query cost—
Total cost per query—

1. Enter vector inventory

Use the number of stored vectors after document chunking and deduplication.

2. Describe vector size

Enter dimensions, numeric precision in bytes, and the number of replicas.

3. Add storage and query prices

Convert provider charges to a per-GB and per-million-query basis where possible.

4. Include fixed capacity charges

Add minimum nodes, reserved compute, platform fees, or other known recurring costs.

5. Review the components

Compare raw vector storage, query cost, total spend, and cost per query.

Replicated storage (GB) = Vectors × Dimensions × Bytes per dimension × Replicas ÷ 1,000,000,000 Storage cost = Replicated storage × Price per GB Query cost = Queries ÷ 1,000,000 × Price per 1M queries Total cost = Storage cost + Query cost + Fixed cost

Where:

  • Dimensions — number of numeric values in each embedding.
  • Bytes per dimension — storage precision, commonly 4 for 32-bit floats before compression.
  • Replicas — number of stored copies included in the estimate.

Assumptions: The storage formula covers raw vector values only. Index overhead and metadata must be represented in fixed cost or an adjusted vector count/rate.

What the result means

The result is a simplified recurring cost estimate for vector storage, queries, and entered fixed charges.

Always compare the model with the provider billing dimensions, minimums, and index overhead.

Given: 5,000,000 vectors, 1,536 dimensions, 4 bytes per dimension, 2 replicas, $0.25 per GB, 2,000,000 queries at $5 per million, and $100 fixed cost.

Calculation: Storage = 5,000,000 × 1,536 × 4 × 2 ÷ 1,000,000,000 = 61.44 GB. Storage cost = $15.36. Query cost = 2 × $5 = $10.

Result: Total estimated cost = $125.36, or about $0.000063 per query.

The fixed capacity charge dominates this example, so optimizing raw storage alone would have limited effect.

Does the storage estimate include metadata?

No. It covers raw vector values and replicas. Add metadata and index overhead through a higher effective storage amount or fixed cost.

What should I use for bytes per dimension?

Use the stored precision after compression or quantization. A standard 32-bit floating-point value uses 4 bytes.

How are replicas treated?

The calculator multiplies raw vector storage by the replica count. Confirm whether your provider bills replicas separately.

Why can the estimate differ from a managed-service quote?

Managed services may charge by provisioned capacity, compute units, minimum pods, or throughput rather than raw GB and query count.

Should embedding generation cost be included?

No. This tool estimates storage and retrieval infrastructure; calculate embedding creation separately.