# Requirements and limitations
<a name="VectorSearch.Requirements"></a>

Keep the following requirements and limitations in mind when you work with vector indexes:
+ Vector indexes use on-demand capacity mode only, and they require a table that also uses on-demand capacity mode. You cannot mix the two capacity modes.
+ Vector embeddings are stored in the index at 32-bit floating point (f32) precision. Higher-precision values are accepted but lose precision when replicated to the index.
+ Fine-grained access control (FGAC) is not supported for the `SearchVectors` API.
+ `SearchVectors` responses are limited to 16 MB. Pagination is not supported. Because each result includes the projected attributes, using `ProjectionType: ALL` with large items and a high `TopK` value can approach this limit. If your items are large, use a narrower projection or reduce `TopK`.
+ Vector indexes do not support `Query` or `Scan` operations. Use the `SearchVectors` API to read from vector indexes.
+ Vector indexes are not accessible through PartiQL. Use the `SearchVectors` API to run similarity searches.
+ Vector indexes are available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions.

For numeric limits including maximum dimensions, TopK range, inline filters per index, indexes per table, and base table size thresholds, see [Vector indexes](ServiceQuotas.md#limits-vector-indexes) in [Quotas in Amazon DynamoDB](ServiceQuotas.md).

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for Amazon DynamoDB. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query amazondynamodb` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).