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There is no established, universal number for the join overhead of storing pgvector embeddings in a separate application table. The cost depends on your schema, indexes, query plan, cache state and workload. To find out whether it matters for your application, compare equivalent layouts with the same queries and measure both using EXPLAIN (ANALYZE, BUFFERS).
First, distinguish a separate logical table—which your SQL may explicitly join—from PostgreSQL’s TOAST mechanism. TOAST can move a large field out of its heap row automatically; it is not an application-designed table keyed by your record ID.
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What “separate” means in Postgres
A second application table
In a separate-table design, the parent record and its vector are stored in different logical tables, usually linked by a shared ID. A query that needs data from both may perform an explicit join. This is a schema choice, and its cost is part of the query plan.
PostgreSQL TOAST storage
PostgreSQL heap tuples cannot span pages. For large variable-length values, PostgreSQL can compress data and/or store it out of line in a TOAST table associated with the owning table. This is managed by PostgreSQL; it does not mean your application has created a second table and must join it by ID. The PostgreSQL 16 documentation describes pages as commonly 8 kB, a normal TOAST threshold and target of about 2 kB, and an 18-byte on-disk TOAST pointer datum. These are storage implementation details, not measurements of application-level join latency. Check the documentation for your deployed PostgreSQL version: PostgreSQL 16: TOAST.
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What the join overhead depends on
A separate vector table can require a lookup when a query needs vector values, but the practical effect depends on the whole plan. Relevant factors include how many rows qualify, the join strategy and indexes, whether pages are cached, which columns the query returns, and the rest of the work performed. A wider logical parent table does not mean every query must fetch the vector value; PostgreSQL’s large-field storage mechanisms are designed to manage such values.
pgvector’s documentation notes that planner cost estimates do not account for out-of-line storage, which can affect plan choices—for example, making a serial scan appear cheaper. It lists inline storage with ALTER TABLE ... ALTER COLUMN embedding SET STORAGE PLAIN as one option and recommends EXPLAIN (ANALYZE, BUFFERS) to investigate performance. That guidance concerns storage and planning; it is not a benchmark establishing the cost of joining two application tables. See the pgvector project documentation.
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Compare both layouts on your workload
Build equivalent versions of the schema: one with the vector on the parent row, and one with the vector in a one-to-one table keyed by the same record ID. Keep the data and test conditions aligned so the results isolate layout differences rather than unrelated changes.
- Match the setup. Use identical data, PostgreSQL and pgvector versions, indexes, predicates, selected columns and result counts. Document the vector-index configuration and whether each search is exact or approximate.
- Test different query needs. Include metadata-only queries that do not return the vector, plus vector-search or retrieval queries that need it. The two designs may affect these query types differently.
- Capture the actual plans. Run
EXPLAIN (ANALYZE, BUFFERS)for each query against each layout and retain the complete plan. Compare execution time, shared-buffer hits and reads, and the scan and join nodes shown in the plan. - Control and report conditions. Record row counts, indexes, cache conditions and concurrency. Repeat runs enough to avoid drawing a conclusion from one noisy timing, and state how the measurements were made.
- Choose by the result that matters. Evaluate metadata reads, vector retrieval or nearest-neighbor queries, storage and index footprint, write and update patterns, and schema complexity or integrity requirements against your actual workload.
How to interpret the result
A timing difference from your test applies to the tested data, query, versions and conditions—not to every Postgres or pgvector deployment. If the separate-table version is slower for one query, inspect its join and buffer activity before attributing the difference to the table split alone. If it is faster or effectively unchanged, that result is likewise specific to the workload tested. The available documentation does not establish a controlled numeric comparison of these two application-level layouts, so do not present storage thresholds or results from another workload as a universal join penalty.
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