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Requirements and limitations

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 in Quotas in Amazon DynamoDB.