Estimating vector Column Storage Before Loading Embeddings

Those short-looking embedding lists add up when the table holds millions of them. Vector column storage grows with the chosen number of dimensions and the number of rows. SQL Server 2025 lets you measure the actual payload and compare it with the table's allocated pages.

Begin With Dimensions and Data Type

SQL Server 2025 introduces the vector data type. A vector definition specifies its dimension count. The default vector elements use single-precision floating-point storage, so the basic element payload grows by four bytes per dimension. Stored value metadata and table overhead need separate accounting.

I start with the dimension count required by the embedding source. Different dimensions are not interchangeable lengths of the same value. You cannot trim a vector to fit a smaller column and assume its similarity meaning remains valid. Keep the source's dimension contract with the table design.

The sample uses small vectors so their JSON array representation remains readable. These arrays are synthetic inputs, not real embeddings or observed storage measurements. Every inserted array must match the destination dimension count and convert to the supported element representation. A short list in a query window looks harmless. The storage bill counts every row, not the elegance of the brackets.

Measure vector column storage separately from ordinary row metadata so the payload and the allocation totals remain understandable.

Measure a Stored Value With DATALENGTH

The following block creates two temporary tables with different dimension counts. Casting the JSON arrays to vector produces the typed values. DATALENGTH measures the stored value returned by that expression. It does not include the entire row, index, page, or allocation overhead.

Compare the values returned on your own SQL Server 2025 instance. Keep the vector definition beside each result so the number has context. The ratio between small examples can reveal fixed overhead as well as per-element growth. Do not turn a single DATALENGTH value into a promise about the entire database footprint.

I use this first check to verify the input contract and payload before planning a large load. If conversion fails, inspect dimension count and element validity. A storage estimate based on arrays that cannot be inserted is not an estimate of the table you will actually have.

CREATE TABLE #VectorSmall(ID int,Embedding vector(3));
CREATE TABLE #VectorLarger(ID int,Embedding vector(6));
INSERT #VectorSmall VALUES(1,CAST('[0.1,0.2,0.3]' AS vector(3)));
INSERT #VectorLarger VALUES(1,CAST('[0.1,0.2,0.3,0.4,0.5,0.6]' AS vector(6)));
SELECT 'Small' AS SampleName,DATALENGTH(Embedding) AS ValueBytes FROM #VectorSmall;
SELECT 'Larger' AS SampleName,DATALENGTH(Embedding) AS ValueBytes FROM #VectorLarger;

Observe Vector Column Storage in a Test Table

A table budget includes the vector, its other columns, row overhead, indexes, and allocated free space. The next setup creates a dedicated permanent test table in a disposable database. That allows sp_spaceused and the allocation DMV to inspect a normal user object.

The chosen dimensions and arrays are demonstration inputs. Replace them with the actual supported dimensions and representative vectors for a useful sizing exercise. Include the real identifier, category, dates, and other columns that will share the row. A vector-only test underestimates a wider production design.

sp_spaceused reports reserved and used space for the object, including its broader allocation accounting. The updateusage option can refresh usage information, but it adds work and is unnecessary for every check. Compare payload observations with table-level space rather than assuming those two measurements should match. They intentionally describe different parts of storage.

CREATE TABLE dbo.VectorStorageDemo
(ID int NOT NULL PRIMARY KEY,CategoryID int NOT NULL,Embedding vector(3));
INSERT dbo.VectorStorageDemo VALUES
(1,10,CAST('[0.1,0.2,0.3]' AS vector(3))),
(2,20,CAST('[0.4,0.5,0.6]' AS vector(3)));
EXEC sys.sp_spaceused N'dbo.VectorStorageDemo';

Separate In-Row and LOB Allocation

sys.dm_db_partition_stats exposes allocated page categories. Group by index_id to distinguish the base table from additional indexes. The row_count column is useful allocation metadata, but it is an approximate count and should not be substituted for a required exact business count.

The following query shows in-row, LOB, row-overflow, used, and reserved pages. Multiply page counts by the documented SQL Server page size when a page-based capacity estimate is needed. Keep the original page counts too, since unit conversions can hide what the DMV actually reported.

As dimensions and row width grow, inspect the observed allocation categories rather than assuming a vector's larger payload follows a particular page layout on every supported definition. A small table also has allocation granularity that makes per-row averages unstable. Test enough representative data to expose the design, and keep the load pattern comparable between alternatives.

SELECT index_id,SUM(row_count) AS ApproximateRows,
 SUM(in_row_data_page_count) AS InRowDataPages,
 SUM(lob_used_page_count) AS LOBUsedPages,
 SUM(row_overflow_used_page_count) AS OverflowUsedPages,
 SUM(used_page_count) AS UsedPages,SUM(reserved_page_count) AS ReservedPages
FROM sys.dm_db_partition_stats
WHERE object_id=OBJECT_ID(N'dbo.VectorStorageDemo')
GROUP BY index_id;

Write the Vector Column Storage Estimate as a Formula

For the default float32 element representation, begin with dimensions multiplied by four for element bytes. Then add measured value metadata, other row columns, row and page overhead, and the indexes required by the workload. Multiply the representative per-row component by the planned row count, and add an allocation allowance based on your own load tests.

What part of the estimate is a formula, and what part is measured? Label those parts separately. DATALENGTH supplies payload evidence. sp_spaceused and partition stats supply object allocation evidence. Planned row growth is an assumption. Keeping those categories distinct makes the budget reviewable.

Do not present a synthetic row count or predicted size as an observed production result. Give the query that produces the real measurement, retain the table definition, and let the reader substitute their own planned population. Storage projections become useful when another person can reproduce both the arithmetic and the inputs.

Revisit the Budget When the Design Changes

Changing dimensions changes the storage contract and usually requires a new embedding representation. Additional indexes, retained historical embeddings, or wider metadata columns also change the budget. Review the full design when those requirements change rather than updating only one dimension number.

Vector indexes are a separate topic. This article estimates stored values and table allocation without including an approximate-index design. Exact-search workload costs also belong to performance testing, not the payload calculation. Storage and search latency answer different planning questions.

After a representative test load, compare the estimate with actual allocation and refine the assumptions. Keep enough free capacity for the loading and maintenance operations the design requires. Clean up the dedicated sample objects in the disposable database. Vector column storage becomes predictable when dimensions, payload evidence, and table-level allocation are reviewed together.

Related reading on this blog: Vector Search in SQL Server 2025 With VECTOR_DISTANCE and Why Table Size Numbers Disagree: Reserved, Used and Unused Space.

An embedding size is not a table size, it is one part of the storage budget.

Published by Pinal Dave on SQLAuthority. More of my work at pinaldave.com.