Vector Database Cost Calculator
Inputs
| Stored Size | 10 GB |
|---|---|
| Price (per GB-month) | 0.25 $ |
| Monthly Base Fee | 0 $ |
Vector Database Cost Calculator
Estimate the monthly and annual cost of hosting a vector index on a managed service from the stored size, the per-gigabyte-month rate, and any flat platform fee.
Inputs
Storage
Pricing
Results
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Details
Vector database cost
Vector database cost is the recurring price of hosting an embedding index so it stays available for similarity search. Unlike embedding, which is a one-time charge to create the vectors, hosting is an ongoing expense: the index occupies storage and serves queries every month it is live. Managed vector databases usually price this as a per-gigabyte-month storage rate, sometimes with a flat platform fee on top, so the cost follows directly from how large the index is.
How hosting is billed
The dominant component is storage. A managed service charges a published rate to keep one gigabyte resident and queryable for a month, and the storage portion of the bill is simply that rate times the size of the index. On top of storage, some services add a flat base fee — for a dedicated instance, a minimum plan, or a reserved pod — that you pay regardless of how full the index is. Adding the two gives the monthly figure, and multiplying by twelve gives the annual run rate.
Query volume is sometimes folded into the storage rate and sometimes metered separately; this model assumes it is included, which matches the common usage-tier pricing where searches are bundled with storage.
The formula
With an index of size gigabytes hosted at a rate of per gigabyte-month and a flat fee of , the storage portion is
Cs=S⋅pgband the monthly and annual totals are
C=Cs+pbase,Cyr=12CWorked example
Suppose an index occupies 18.5 GB, hosted at per gigabyte-month with a monthly platform fee for a dedicated pod. The storage portion is
Cs=18.5×0.25=4.63so the monthly cost is
C=4.63+70=74.63and the annual run rate is . Here the base fee dwarfs the storage charge, which is typical for small indexes on a dedicated plan — the fixed platform cost, not the gigabytes, sets the bill. Usage-based pricing without a base fee would charge only the $4.63 storage portion.
Notes and variations
The estimate covers storage-based hosting. Depending on the provider, you may also pay for query throughput, network egress, or compute for index builds, none of which scale with stored size. Quantising vectors or reducing the embedding dimension shrinks the index and therefore the storage portion, but has no effect on a flat base fee. Self-hosting an open-source database trades the managed premium for operational work and the raw cost of the underlying disk or memory.
Application
The stored size that drives this cost comes from Vector Database Size Calculator, which turns a vector count, dimension, and precision into a gigabyte figure. The vectors themselves are created by the one-time embedding step priced in Embedding Cost Calculator, while the generation calls that query the index are costed by Token Cost Calculator.
Frequently Asked Questions (FAQ)
What does the hosting fee cover?
A managed vector database charges for keeping the index available and queryable around the clock. The per-gigabyte-month rate covers storing the vectors and serving similarity searches against them; the optional base fee covers a dedicated instance or a minimum plan. Query volume can be billed separately on some platforms, so check whether searches are included in the storage rate or metered on top.
Is self-hosting cheaper than a managed service?
Self-hosting an open-source vector database on your own server avoids the managed per-gigabyte premium, and at large scale the raw infrastructure cost can be lower. Against that, you take on the work of provisioning, scaling, backups, and uptime. A managed service folds those into the rate, which is why its per-gigabyte price is higher. The break-even depends on index size and how much operational effort you can absorb.
How can I lower the storage cost?
Storage cost scales with the size of the index, so the levers are the same ones that shrink the index: quantising vectors from 32-bit floats to 8-bit integers cuts the footprint to a quarter, and using a model with a smaller embedding dimension reduces it proportionally. Removing duplicate or stale documents lowers the vector count directly. Each of these reduces gigabytes stored, and the bill follows.
Disclaimer
This estimates storage-based hosting cost only and may exclude query, bandwidth, or compute charges that some services bill separately. Published rates change over time and vary by provider, region, and plan. Confirm current pricing with your provider before budgeting.