A vector database stores embeddings and serves similarity search for retrieval augmented generation and semantic search. Its cost compares across two axes. The first is the deployment model: a fully managed vector service, a managed vector feature inside a database you already run, or a self hosted engine on your own cloud compute. The second is the workload shape: how many vectors, how many dimensions each carries, which index you choose, and how high a recall you demand. The same corpus can differ several fold in cost depending on those choices, which is why comparing services on list price alone is misleading. The right comparison fixes your workload and reads the total cost of memory, compute, and storage each option needs to serve it.
Here is what drives the bill, how the options stack up, and where the savings sit.
What actually drives vector database cost?
- Vector count and dimensionality. Memory scales with vectors multiplied by dimensions. Ten million vectors at 1536 dimensions need far more RAM than the same count at 384 dimensions. Smaller embedding models cut both storage and the memory the index must hold.
- Index type. An in memory graph index such as HNSW is fast but memory hungry. A quantized or disk backed index trades a little recall and latency for a large drop in memory cost. The index choice often moves the bill more than the service choice.
- Recall target. Chasing the last few points of recall forces larger indexes and more compute. Accepting a recall appropriate to the use case, rather than the maximum, is one of the cheapest levers available.
- Query volume and latency. High query rates and tight latency targets drive replica count and instance size. Provisioned throughput and capacity reservations for AI workloads need their own governance, the same discipline the cross cloud cost optimization guide applies to commitments generally.
How do managed and self hosted options compare?
The trade is the familiar one between operational simplicity and unit cost. A fully managed vector service removes the work of running the engine and scales on demand, at a premium per stored and queried vector. A managed vector feature inside a database you already operate avoids a new system and can be efficient if your corpus is modest. A self hosted open source engine on your own compute has the lowest unit cost at scale and the highest operational burden, and it lets you apply your existing commitment coverage to the underlying instances. The decision is a function of scale and team capacity: small corpora and lean teams favour managed; large, steady, performance sensitive corpora with platform engineering favour self hosted on reserved or committed compute.
A cost comparison table
| Option | Cost model | Best fit | Main cost lever |
|---|---|---|---|
| Fully managed vector service | Per stored vector plus per query, on demand | Small to mid corpora, lean teams, spiky load | Vector count and query volume |
| Vector feature in existing database | Bundled into instance you already run | Modest corpora alongside transactional data | Instance size and index memory |
| Self hosted engine on your compute | Underlying compute, memory, and storage | Large, steady, latency sensitive corpora | Index type and committed compute coverage |
Indicative comparison. Actual cost depends on vector count, dimensionality, index choice, recall target, and query rate, which is why every comparison should be run against your own workload.
Where are the savings most teams miss?
Three levers are routinely left on the table. The embedding model is the first: moving from a high dimension model to a smaller one that meets your retrieval quality cuts memory and storage across the whole index at once. Quantization is the second: product or scalar quantization shrinks the index for a small, measurable recall cost that many use cases never notice. The third is commitment coverage on self hosted deployments, where the underlying instances qualify for the same Savings Plans, Reservations, committed use discounts, or Universal Credits as any other compute, yet teams often run them on demand. Tuning recall to the use case, rather than to a benchmark, ties these together.
A worked example
A Fortune 500 retailer ran semantic product search on a fully managed vector service with roughly 30 million vectors at 1536 dimensions and a recall target set near the maximum. The managed per vector cost at that scale was the dominant line. We tested a smaller embedding model that held retrieval quality, applied scalar quantization, and moved the workload to a self hosted engine on instances already covered by committed use discounts, with the recall target set to what the product experience actually required rather than the benchmark ceiling. Memory per vector fell, the index shrank, and the underlying compute moved onto existing commitments. The vector search line dropped substantially and fed the broader program. Figures are verified against billing data and anonymised.
Frequently asked questions
What drives vector database cost the most?
Is a managed vector service cheaper than self hosting?
How do you cut vector database cost without losing quality?
Compare your vector workload options with us
We help enterprises size and place vector search and the wider AI stack across AWS, Azure, GCP, and OCI, comparing managed and self hosted options against your real workload rather than list price. We take zero provider commissions and work on a Fixed Fee or a no risk Gainshare basis, guaranteed: we reduce your cloud spend or we reimburse our service fee. Book a strategy call to model your options.
Put a defensible number on your cloud spend.
No provider in the room, no published price list. Tell us your footprint and we will scope the savings against your billing data — we reduce your cloud spend or we reimburse our service fee.
The Cloud Spend Navigator: what changed in cloud pricing, commitments, and FinOps — no vendor spin.