Vector Database Sizing Calculator: Storage, Index Overhead, and Cost
This free vector database sizing calculator converts vector count, embedding dimensions, and precision into an actual storage footprint, and it is built for platform engineers and IT directors evaluating on-prem or cloud vector databases. Enter how many vectors you plan to store, their dimensionality and numeric precision, expected index overhead, and how many replicas you need for availability, and the tool returns per-node storage, total cluster storage, and monthly cost. Teams routinely underestimate vector storage by 30-60% because they price the raw vectors and forget the index structure and replication factor.
Your numbers
Total chunk embeddings you plan to store, roughly equal to your document corpus chunk count.
Set by the embedding model you choose. Larger models generally use more dimensions.
Lower precision cuts storage substantially, usually at a small and measurable recall cost.
Graph-based indexes like HNSW typically add 30-60% on top of raw vector storage.
Total copies of the index across nodes, including the primary, for redundancy and read throughput.
Blended cost of the fast local disk or SSD storage backing the vector database, on-prem or cloud.
Your results
Storage sizing only. Vector database compute, query serving, and reranking infrastructure are priced separately; see the hybrid search cost calculator for the serving side.
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How the storage math works
Raw vector storage is vector count times dimensions times bytes per dimension. With the defaults, 5 million vectors at 1536 dimensions and 4 bytes per dimension (float32) produce about 30.7 GB. Index overhead accounts for the graph or tree structures that make approximate nearest neighbor search fast, most commonly HNSW, which typically adds 30-60% on top of the raw vectors, bringing this example to roughly 43 GB per copy. Multiplying by a replica count of 2 for availability and read scaling gives about 86 GB total, and at $0.10 per GB per month that is roughly $8.60 monthly in storage cost alone.
- Raw storage = vector count times dimensions times bytes per dimension.
- Index overhead typically adds 30-60% on top of raw vector storage for HNSW-style graphs.
- Total storage scales linearly with replica count, which you need for both availability and query throughput.
- This calculator covers storage only; query-serving compute is a separate infrastructure line.
Precision and quantization tradeoffs
Precision is the highest-leverage lever in this calculator because it scales storage linearly. Moving from float32 to int8 scalar quantization cuts storage 4x with a recall loss most enterprise evaluations measure at 1-3%. Binary quantization goes further, cutting storage 32x compared to float32, but recall loss becomes workload-dependent and needs real evaluation before production use, not a published benchmark. A common and pragmatic pattern is storing full-precision vectors for reranking a small candidate set while using quantized vectors for the initial fast approximate search over the full corpus.
- Float32 to float16 halves storage with minimal measurable recall impact for most embedding models.
- Int8 scalar quantization cuts storage 4x and typically costs 1-3% recall, worth testing against your own eval set.
- Binary quantization cuts storage up to 32x but needs workload-specific validation before trusting it in production.
- A common pattern reranks a quantized-search candidate set using full-precision vectors for the final ranking.
Planning for growth and redundancy
Corpus growth is the number most sizing exercises get wrong, because document collections rarely stay static and re-indexing a live production vector database under load is disruptive. Budget headroom for at least 12-18 months of corpus growth rather than sizing to today's document count exactly. Replica count is not optional in production: a single node with no replica is a single point of failure for the entire retrieval layer, and most enterprise deployments run at least two replicas, with three or more for high query volume where replicas also serve reads in parallel.
How Netray sizes vector infrastructure for regulated manufacturers
Netray designs on-prem retrieval infrastructure for aerospace, defense, and electronics manufacturers where the vector database, like everything else in the stack, has to run inside the customer's network. We benchmark quantization strategies against your actual document corpus rather than published leaderboards, size replica counts to your real query volume and availability requirements, and integrate the result with entitlement-aware retrieval so the vector store never becomes a way to bypass ERP and document management permissions. Engagements typically start with a corpus and query-pattern assessment before any hardware is procured.
Frequently Asked Questions
How much storage does index overhead actually add?
For HNSW, the most common approximate nearest neighbor index, overhead typically runs 30-60% on top of raw vector storage, driven mainly by the graph connectivity parameter and how thoroughly the index is built. Higher connectivity improves recall and query speed but increases both index size and build time. IVF-based indexes generally carry lower overhead than HNSW but trade off some query latency, so the right choice depends on whether your workload is more sensitive to storage cost or to query speed.
Is quantization safe for a production RAG system?
Generally yes for int8 scalar quantization, which most enterprise evaluations show costs only 1-3% recall while cutting storage 4x. Binary quantization is more aggressive and its impact varies significantly by embedding model and content type, so it needs validation against your own golden question set rather than a vendor's published benchmark. A safe rollout pattern quantizes for the initial candidate search and reranks the top results using full-precision vectors, capturing most of the storage win with minimal accuracy risk.
How many replicas do I actually need?
At minimum two for basic availability, so a single node failure does not take down retrieval entirely. Production systems serving meaningful query volume commonly run three or more replicas, both for redundancy and because most vector databases can serve reads from any replica, effectively multiplying query throughput. Size replica count against your uptime requirement and your peak queries-per-second target together, not availability alone.
Does this calculator cover the compute needed to serve queries?
No, this tool sizes storage only. Query serving requires CPU or GPU compute for the approximate nearest neighbor search itself, plus additional compute if you run a keyword index or reranker alongside it. Use the hybrid search cost calculator to size the compute and infrastructure cost for a combined vector and keyword search stack once you know your storage footprint from this tool.
Get a right-sized vector database architecture with quantization strategy and replica planning included.
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