A pgvector vector value uses 4 × dimensions + 8 bytes; halfvec uses 2 × dimensions + 8 bytes. Those figures describe the vector value alone—not the full table, indexes, or total database storage. Use them for a first-pass payload estimate, then measure a representative table and index in PostgreSQL.
Calculate the storage for one embedding
pgvector stores vector elements as single-precision values and halfvec elements as half-precision values. The documented formulas are:
vector:4 × dimensions + 8byteshalfvec:2 × dimensions + 8bytes
The following values are arithmetic from those formulas, not benchmark measurements. The extension’s README documents the types and storage limits.
| Dimensions | vector value |
halfvec value |
|---|---|---|
| 384 | 1,544 bytes | 776 bytes |
| 768 | 3,080 bytes | 1,544 bytes |
| 1,536 | 6,152 bytes | 3,080 bytes |
| 3,072 | 12,296 bytes | 6,152 bytes |
For a rough value-only estimate, multiply the per-embedding size by the expected number of rows. For example, one million 768-dimensional vector values amount to 3.08 billion bytes by this arithmetic, before table and index overhead. That is not a forecast of provisioned disk usage.
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Why the vector formula is not your database size
A table also contains row and page overhead, other columns, and possibly indexes and TOAST data. The formula gives a useful baseline for vector payload; it does not establish a universal overhead ratio. PostgreSQL provides different size functions for a value, table, indexes, and combined relation size. Its database size functions documentation explains these measurements.
pg_column_sizereports the storage size of an individual value. Applied directly to a column value, it can reflect compression.pg_table_sizemeasures table storage.pg_indexes_sizemeasures the indexes attached to a table.pg_total_relation_sizeincludes the table, its indexes, and TOAST data.
Run these queries against a representative loaded dataset and your actual schema:
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-- Size of one stored embedding value
SELECT pg_column_size(embedding)
FROM items
WHERE embedding IS NOT NULL
LIMIT 1;
-- Table storage, indexes, and combined total
SELECT
pg_size_pretty(pg_table_size('items')) AS table_size,
pg_size_pretty(pg_indexes_size('items')) AS indexes_size,
pg_size_pretty(pg_total_relation_size('items')) AS total_size;
-- Size of one named index
SELECT pg_size_pretty(pg_relation_size('items_embedding_hnsw'));Use the documented formula to plan the raw value payload and PostgreSQL’s size functions to inspect observed storage. A single value measurement is not a substitute for sizing the table and its intended indexes.
Account for search indexes and memory
pgvector uses exact nearest-neighbor search by default. HNSW and IVFFlat provide approximate search, trading recall behavior for speed. They add storage, and their actual sizes depend on the data and index settings, so build the intended index on representative data and measure it.
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| Approach | Search behavior | Build and memory considerations |
|---|---|---|
| Exact search | Exact nearest neighbors; pgvector’s default | No approximate index required for the search itself |
| HNSW | Approximate; the project describes a better speed/recall tradeoff than IVFFlat | Slower builds and greater memory use than IVFFlat, according to the project |
| IVFFlat | Approximate; trades recall behavior for speed | Compared with HNSW, the project describes faster builds and lower memory use |
The pgvector README says indexes do not have to fit in memory, though performance is likely better when they do. A pgvector discussion dated October 3, 2024 includes a user-reported example in which indexes for one million 768-dimensional vectors were close to 3.9 GB each with particular IVFFlat and HNSW settings. A maintainer explained that index records include vector data and that HNSW also stores neighbor references. Those figures describe that reported setup only; they are not a general index-size multiplier.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a representation your index supports
halfvec cuts the element storage roughly in half compared with vector, but it changes precision. The documented size difference does not guarantee equivalent retrieval quality for every workload. Validate retrieval results and application behavior on representative data before changing the stored representation.
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Dimension and type also affect which indexes are available. The pgvector README documents vector values up to 16,000 dimensions, while its listed HNSW limits are 2,000 dimensions for vector and 4,000 for halfvec. It lists bit indexing up to 64,000 dimensions. Check the extension version and supported type/index combination for your deployment before choosing a schema. The README also describes half-precision indexing, binary quantization, subvector indexing, and dimensionality reduction as options to consider for smaller indexes or higher-dimensional workloads.
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A practical workflow for estimating capacity
- Confirm the dimensions and row count. Use the embedding model’s output dimension and estimate how many rows you expect to store.
- Calculate the value payload. Apply
4 × dimensions + 8forvector, or2 × dimensions + 8forhalfvecif that representation suits the application. - Multiply by expected rows. Label the result as a value-only estimate, not a total disk requirement.
- Load representative rows. Measure table, index, and total relation sizes with PostgreSQL’s size functions.
- Build the intended index. Record its actual size; if updates and deletes are part of the workload, measure again after representative activity.
- Compare quality and operating behavior. Measure storage and query behavior before changing precision or index type.
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