Question 1

Why does the storage-only “sticker” number undershoot the real vector database bill?

Accepted Answer

Most vector database calculators — including our own Vector Database Storage Cost Calculator — answer one narrow question: how many gigabytes do your vectors occupy, and what does that cost per GB-month. That is a real number, but it is only one of four recurring line items on an actual bill. Egress bandwidth is charged every time vectors, query results or a full export cross out of the provider's network, commonly $0.08–$0.09/GB on AWS-typical cross-region or internet egress, and it scales with usage, not just storage size. Reindexing is charged whenever you rebuild the index — most visibly after an embedding-model upgrade — and documented compute costs run roughly $12–$40 per 10 million vectors for a full rebuild. Backup and snapshot storage is billed at the same $/GB-month rate as primary storage, once per retained copy, and multi-region or disaster-recovery setups routinely keep two or three copies rather than one. None of those three show up in a calculator that only multiplies vectors by dimensions by price per GB, which is exactly why documented industry analysis of Pinecone, Weaviate and Qdrant pricing gaps (Actian's cost-breakdown blog and 2026 “Vector DB Bills Exposed” reporting) puts the real bill at 2.5x to 4x the storage-only sticker number once egress, reindexing and backup are added back in.

Question 2

Why does upgrading my embedding model trigger a full reindex cost?

Accepted Answer

Because vectors from two different embedding models don't live in the same geometric space, so you cannot mix them inside one index and still get meaningful nearest-neighbor results. A vector produced by an older embedding model and a vector produced by its newer replacement can both be 1536 numbers long and still point in directions that have nothing to do with each other's semantic meaning — similarity search across a mixed index would silently return garbage. The only correct migration path is to re-embed the entire corpus with the new model and rebuild the index from scratch, which is real compute cost (documented in the $12–$40 per 10 million vectors range) that lands on your bill every time you decide a newer or better embedding model is worth adopting — not a one-time setup cost, but a recurring line item every time the underlying model changes.

Question 3

Why do backup copies multiply the storage line, and how does that push the real multiplier toward 4x?

Accepted Answer

Because a backup or snapshot is a full copy of the index, and most vector database providers bill each retained copy at the same $/GB-month rate as primary storage — there is no discount for the fact that the data is redundant. One backup copy simply doubles the storage-related spend (primary plus one backup, both at full price); two backup copies for a multi-region or disaster-recovery setup triples it. In the worked example on this page, moving from one backup copy to three takes the backup line from $103.68/mo to $311.04/mo while every other cost stays fixed, which is enough on its own to push the hidden cost multiplier from 2.26x to 4.26x — right in the commonly-cited “up to 4x” range documented for real-world Pinecone, Weaviate and Qdrant deployments once multi-region redundancy is added.

Question 4

How is this different from the Vector Database Storage Cost Calculator already on this site?

Accepted Answer

The Vector Database Storage Cost Calculator is deliberately storage-only: vectors times dimensions times bytes per dimension times index overhead times $/GB-month, with zero mention of egress, reindexing or backup — it answers “what do my raw vectors cost to store,” which is a real and useful number for sizing a corpus. This calculator reproduces that exact same number as its “sticker” baseline (base storage cost), then adds the three commonly-omitted cost lines on top of it — monthly egress bandwidth, the amortized annual cost of reindexing after an embedding-model migration, and backup copies billed at the same storage rate — to show what the real monthly bill looks like once those recurring costs are included. Use the storage calculator to size the corpus; use this one to see what it actually costs to run in production.

ベクトルデータベースの隠れコスト乗数計算ツール

コーパスとストレージ

下りとインデックスの再作成

コンポーネント別のコスト内訳

1 つのバックアップコピーと 3 つのバックアップコピー

この計算機の仕組み

数字の読み方

よくある質問

ストレージのみの「ステッカー」の数値が実際のベクトルデータベースの請求額を下回るのはなぜですか?

埋め込みモデルをアップグレードすると、完全な再インデックスのコストが発生するのはなぜですか?

バックアップコピーによってストレージラインが増加するのはなぜですか?また、実際の増加率はどのようにして 4 倍に近づくのでしょうか?

これは、このサイトにすでにある Vector Database Storage Cost Calculator とどう違うのですか?