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How much space does one pgvector embedding use?
The documented formulas are 4 × dimensions + 8 bytes for vector and 2 × dimensions + 8 bytes for halfvec. vector stores single-precision elements; halfvec stores half-precision elements. The figures below are arithmetic from those formulas, not benchmark measurements. pgvector documentation
| 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 quick payload estimate, multiply the per-value figure by the number of rows that will contain embeddings. For example, 100,000 rows with 768-dimensional vector values yield 308,000,000 bytes of vector-value payload by the documented formula. That is not a forecast of provisioned disk: it excludes row and table overhead, indexes, other columns, and storage details.
Why the vector formula is not your database size
The formula estimates the vector value, while PostgreSQL stores values within tables and may also store indexes and TOAST data. PostgreSQL provides separate size functions for inspecting those layers. pg_column_size reports the bytes used by an individual value and can reflect compression when called on a column value; pg_indexes_size measures attached indexes; pg_total_relation_size includes the table, its indexes, and TOAST data. PostgreSQL size functions
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Run these queries against a representative loaded table and your actual schema:
-- Size of one stored embedding value
SELECT pg_column_size(embedding)
FROM items
WHERE embedding IS NOT NULL
LIMIT 1;
-- Table storage, attached 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'));The query results show observed storage for the database objects you measure; they are more useful for capacity planning than treating the vector-value formula as an exact disk requirement. Measure after loading enough representative data, and include the index you expect to use.
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How much extra storage can a vector index add?
pgvector uses exact nearest-neighbor search by default. HNSW and IVFFlat provide approximate nearest-neighbor search, trading recall behavior for speed. Indexes can add substantial storage, but there is no universal multiplier: actual size depends on the data and index settings, so build the intended index and measure it.
| Approach | Search behavior | Storage and operational considerations |
|---|---|---|
| Exact search without an approximate index | Exact nearest-neighbor search by default. | No HNSW or IVFFlat index is added; measure the table and any other indexes you retain. |
| HNSW | Approximate search; pgvector describes a better speed/recall tradeoff than IVFFlat. | Builds more slowly and uses more memory than IVFFlat, according to the project documentation. Measure the resulting index on your data. |
| IVFFlat | Approximate search; speed and recall depend on index configuration. | Compare its measured index size and query behavior with HNSW under your workload. |
A pgvector project discussion dated October 3, 2024 reported close to 3.9 GB for each of an IVFFlat and an HNSW index on one million 768-dimensional vectors under particular settings. A maintainer explained that index records include vector data and, for HNSW, neighbor references. This is a single settings-specific example, not a sizing multiplier for other workloads. pgvector project discussion, October 3, 2024
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Indexes do not have to fit in memory, though pgvector notes that performance is likely better when they do. pgvector documentation
Should you use vector or halfvec?
At the same dimension count, halfvec uses half the element storage shown by the documented formulas. That can reduce the vector payload and may help reduce the working set, but it changes numeric precision. Test retrieval quality and application behavior using representative data before adopting it; the documented storage sizes do not establish equivalent quality for every workload.
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What if your embeddings have more dimensions?
The pgvector README documents vector values up to 16,000 dimensions. The listed HNSW index limits are up to 2,000 dimensions for vector and 4,000 for halfvec; bit indexing is listed up to 64,000 dimensions. These limits differ by type and index, so check the extension version and supported type/index combination for your deployment before selecting a schema. pgvector documentation
For smaller indexes or larger-dimensional workloads, the project documentation also describes half-precision indexing, binary quantization, subvector indexing, and dimensionality reduction. These are design alternatives rather than interchangeable storage guarantees; evaluate their retrieval behavior and application requirements with your data.
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A practical sizing workflow
- Confirm dimensions and row count. Check the embedding model’s output dimension and estimate how many rows will store a non-null embedding.
- Calculate value-only payload. Use
4 × dimensions + 8forvector, or2 × dimensions + 8forhalfvecif that representation fits your requirements. - Multiply by expected rows. Label the result as a vector-value estimate, not total database storage.
- Load representative records. Use the real PostgreSQL version and schema, then inspect value, table, index, and total relation sizes with the size functions above.
- Build the intended index. Measure its actual size after creation; if updates and deletes are part of the workload, recheck after representative activity.
- Compare tradeoffs before changing design. Evaluate storage alongside query speed, recall behavior, build time, memory use, precision, and application behavior.
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