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A filtered S3 Vectors query can return fewer than the requested number of results without an error, and pre-filtering does not guarantee the exact nearest matches in every case. In Ajay Kumar’s October 2026 synthetic benchmark, AWS’s ENHANCED mode returned complete, exact results for filters matching five or fewer vectors—but its recall fell for filters matching 50 or 135 vectors as K increased. The test compared ENHANCED with locally computed exact results and a client-side post-filter baseline; it did not measure real CLASSIC mode.
Why a filtered vector query can return fewer than K results
A vector search ranks items by similarity to a query vector. A metadata filter narrows which items are eligible. If a query asks for K results but only a few eligible items are found during the search, it can return fewer than K. That is an underfilled result set, not necessarily an error.
AWS describes two S3 Vectors modes. In CLASSIC, filtering and candidate search happen in tandem, so a query can return fewer than top K when few vectors match. In ENHANCED, the service applies the metadata filter first and searches only matching vectors. AWS calls this pre-filtering: Metadata filtering.
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What Kumar tested—and what the comparison means
Ajay Kumar, identified by DEV Community as an AWS Community Builder, published a benchmark on October 6, 2026. He loaded 50,000 synthetic vectors with 384 dimensions and cosine distance into one disposable ENHANCED index in us-east-1 on October 5. The test used 50 query vectors generated from the same 64 clusters as the dataset, but from a separate random stream. Metadata labels were independent of vector positions.
Filters ranged from broad to empty, including ones matching 25,000, 50, 135, five, three, one, or zero vectors. For exact ground truth, Kumar filtered the canonical data locally, computed exact cosine distances for every eligible vector, and selected the nearest K. He then compared ENHANCED’s responses with that exact set.
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The client-side baseline was not S3 Vectors CLASSIC. It queried broadly at budgets of 100, 1,000, or 10,000 results, then applied the filter locally. Kumar reports 6,000 filtered ENHANCED logical requests, 600 unfiltered ANN reference requests, and 300 client-side post-filter requests. Because he could not run CLASSIC on a new bucket, these measurements are not a direct mode-versus-mode comparison.
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Results: the most selective filters were exact, but not all filters were
Filters matching five or fewer vectors
For filters with five or fewer matches, Kumar reports ENHANCED Recall@K of 1.000 at every tested K, with complete result sets. The client-side baseline could still miss eligible vectors even when it queried 10,000 candidates: at K=10, it found the one FONE match for 42% of queries and found an average of 24% of the five F001 matches. These are results for this dataset and query set, not guarantees for other workloads.
Filters matching 50 or 135 vectors
The results were less exact at these selectivities, and recall declined as K rose:
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| Filter | Eligible vectors | Recall@5 | Recall@10 | Recall@20 | Recall@50 |
|---|---|---|---|---|---|
| F01 | 50 | 0.948 | 0.898 | 0.811 | 0.610 |
| FAND2 | 135 | 0.952 | 0.932 | 0.883 | 0.724 |
These are Kumar’s reported results across 50 query vectors in the single-index benchmark. At F01/K=50, ENHANCED returned an average of 30.52 of the 50 eligible vectors. The author reports identical result sets across three repeats and does not establish why the responses were incomplete.
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At K=10, ENHANCED recall was 0.984 for F50 and 0.992 for F10, compared with 0.994 for the unfiltered approximate-nearest-neighbor reference. On broad filters, the client-side post-filter baseline often matched ENHANCED when its query budget was large enough, although it used paginated requests.
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What the test does—and does not—say about AWS’s five-times claim
AWS’s September 30, 2026 launch post says pre-filtering can return “up to 5x more of the matching vectors” on highly selective filters. That is AWS’s launch claim, not a fivefold Recall@K improvement measured by Kumar. His benchmark did not obtain a real CLASSIC result, and AWS did not publish the methodology behind the launch figure. The comparison therefore cannot confirm or refute that claim.
The benchmark supports a narrower conclusion: ENHANCED matched exact ground truth for the tested filters with five or fewer matches, but it was not exact for every selective filter. Selectivity alone is not enough to predict recall; the reported F01 and FAND2 results differ by K and by the number of eligible vectors.
How to validate ENHANCED for your workload
AWS advises testing with representative data and queries because query work depends on index size, the fraction of vectors that match, and filter complexity. AWS also notes that average recall can vary with embedding model, dataset size and dimensions, and query distribution. Use a local exact baseline on a representative sample rather than transferring Kumar’s scores to a production workload.
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- Build an exact comparison set. For representative queries, apply the same metadata filter to your canonical data, compute exact distances among eligible vectors, and record the top K.
- Measure ranking and underfill separately. Calculate Recall@K against the exact set and completeness against the number of available eligible slots. A short response and a poorly ranked response are different failure modes.
- Vary selectivity and filter complexity. Include broad, selective, and empty filters, multiple K values, and the number and type of constraints used in production.
- Measure latency with its scope stated. If timing client requests, include that this includes network and client overhead; do not label it AWS server latency.
- Check the filter limit. ENHANCED supports up to 100 filter constraints per query, with each evaluated value counting toward the limit. A filter with more than 100 values may need consolidation or multiple queries whose results are merged. See AWS’s metadata-filtering documentation and querying vectors documentation.
What changes when you enable ENHANCED
AWS announced metadata pre-filtering on September 30, 2026. New indexes in buckets created on or after that date use ENHANCED by default. Older buckets retain CLASSIC unless the bucket default or index mode is changed. AWS says an existing index can be moved in place with UpdateIndexMode; existing vectors do not need to be re-ingested, and query calls do not need to change. AWS senior specialist solutions architect Daniel Abib described the migration in the launch post: “Pre-filtering takes effect in place.” See the AWS launch announcement.
ENHANCED also supports the $startsWith operator for string-prefix filters, such as document paths, URLs, or hierarchical keys. AWS documents equality, inequality, numeric comparisons, set membership, existence checks, and $and/$or as other filter operators. AWS says pre-filtering adds no separate charge; standard S3 Vectors charges for storage, PUT requests, and queries still apply.
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