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Amazon OpenSearch Service can compress vectors to reduce memory use, but the right setting depends on the collection or index mode, acceptable recall and latency, and whether you can rebuild existing indexes. For NextGen Serverless vector collections, AWS documents 32x compression by default, with 1x, 2x, 8x, 16x, and 32x options. Disk-optimized search is a separate configuration with its own compression choices and a two-stage retrieval process.
What native vector compression does
Vector compression stores a smaller representation of vectors for search, reducing the memory required by the index. That reduction can come with changes to recall, latency, and supported query behavior, so a compression ratio is not enough to choose a setting by itself.
OpenSearch Service documentation describes more than one relevant configuration. NextGen Serverless vector collections have a documented compression-level setting; disk-optimized search uses compressed vectors in memory to find candidates and reads full-precision vectors from disk to rescore them. These are related approaches, not interchangeable names for one setting.
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NextGen Serverless vector collections
AWS documents 32x compression as the default for NextGen vector collection indexes. The collection documentation lists 1x, 2x, 8x, 16x, and 32x as selectable values for compression_level. NextGen mappings do not require engine or mode parameters because the system selects the configuration internally. See AWS’s vector search collection documentation.
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AWS documents a 10-second read-after-write latency through the refresh_interval for these collections. It also states that radial search is unsupported on NextGen vector indexes using 32x compression. If radial search is part of your query design, check this limitation before selecting that setting.
Disk-optimized search
AWS describes disk-optimized search as keeping compressed vectors in memory for candidate generation, then retrieving full-precision vectors from disk for final rescoring. The cost-optimization documentation lists 2x (FP16), 4x (byte), 8x, 16x, and 32x (binary) levels for this mode. This list differs from the NextGen collection options; do not assume the settings apply to both contexts. See AWS’s cost-optimization guidance.
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AWS characterizes this on-disk mode as useful when reducing cost matters more than single-digit-millisecond response times, and says workloads that tolerate P90 latency in the 100–200 ms range may suit it. This is AWS’s guidance for the documented mode, not a service-wide latency guarantee.
Compare the documented trade-offs
| Configuration or evidence | What AWS documents | What to consider |
|---|---|---|
| NextGen Serverless vector collections | Default 32x; selectable 1x, 2x, 8x, 16x, or 32x compression | Check feature support, including the radial-search limitation at 32x. |
| Disk-optimized search | 2x (FP16), 4x (byte), 8x, 16x, or 32x (binary); compressed candidates in memory, full-precision rescoring from disk | Balance reduced memory use against recall and a workload’s tolerance for latency. |
| Cohere TREC-RAG benchmark | AWS reports recall@100 of 0.94, P90 latency of 104 ms, and cost reduction of 83%. | AWS-reported dataset result, not a prediction for your vectors or workload. |
| Tasb-1M benchmark | AWS reports recall@100 of 0.96, P90 latency of 7 ms, and cost reduction of 67%. | AWS-reported dataset result, not a prediction for your vectors or workload. |
| Marco-1M benchmark | AWS reports recall@100 of 0.99, P90 latency of 7 ms, and cost reduction of 67%. | AWS-reported dataset result, not a prediction for your vectors or workload. |
The benchmark figures are reported in AWS’s current cost-optimization documentation; AWS does not state a publication date for these results. The same page says default 32x binary quantization in disk-optimized mode reduces memory requirements by 97% versus in-memory mode. Treat that as AWS’s documented comparison, not a guaranteed reduction for every deployment. Dataset, query mix, index configuration, and operating conditions affect results.
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Validate quality and latency before choosing
Set target thresholds before testing so a lower memory footprint does not obscure an unacceptable search-quality or response-time change. Use a representative dataset and query mix, then compare the available configurations in the deployment context you intend to run.
- Memory or cost: measure the actual reduction for your collection or index rather than assuming the published figure will transfer.
- Recall and search quality: evaluate against relevant results for your application and decide what degradation, if any, is acceptable.
- Latency: measure P90 under representative load and compare it with the response-time requirement for the workload.
- Representation and ratio: confirm which compression levels are available in the exact mode; the documented level lists differ.
- Query support: verify that required query features work with the selected configuration.
- Deployment compatibility: confirm deployment type, service or engine version, and region requirements.
- Indexing and migration: account for index building, compatibility constraints, and the work of moving existing data.
AWS publishes benchmarks for three datasets, but those figures do not identify a universally best setting. Your own measurements should determine whether the memory or cost savings justify any changes in recall or latency.
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Keep GPU indexing acceleration separate from compression
GPU acceleration concerns vector index building, not whether a query-time compression level is supported. AWS says GPU acceleration is supported on OpenSearch domains running version 3.1 or later and on OpenSearch Serverless collections. Its compatibility documentation lists Faiss as supported for GPU acceleration and lists Faiss product quantization, IVF, NMSLIB, and Lucene as unsupported for that acceleration. Those exclusions apply to GPU acceleration; they are not blanket statements that those methods or engines cannot be used for vector search. Check AWS’s GPU acceleration compatibility guidance.
Plan reindexing and test changes safely
AWS says applying the documented disk-optimized change to existing indexed data requires reindexing into a new index. Treat this as a migration: plan capacity and build time, keep the current index available while validating the replacement, and test with representative data before production use.
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- Define acceptance criteria. Record the recall, P90 latency, and memory or cost targets the new index must meet.
- Build a representative test. Use data and queries that reflect production, and test in a non-production environment.
- Compare candidate settings. Measure search quality, latency, and resource use for the configuration appropriate to your deployment mode.
- Reindex when required. For the documented disk-optimized change to existing data, create a new index and reindex rather than expecting the existing index to switch in place.
- Validate before cutover. Confirm query behavior and operational requirements against the acceptance criteria, then move traffic only when the replacement is ready.
AWS’s general indexing guidance advises testing a representative workload in a non-production environment before using a new index codec setting, because changing an existing index codec can create challenges. This is general codec advice, not a vector-quantization-specific procedure. Read the OpenSearch Service indexing guidance alongside the documentation for the compression mode you plan to use.
Use automated tuning only when its constraints fit
AWS Auto-optimize evaluates configurations against user-defined latency and recall requirements and returns up to three recommendations. It supports HNSW-based indexes. AWS lists managed domains on OpenSearch versions 2.19, 3.1, and 3.3, and documents engine and regional constraints. Check those eligibility details in the Auto-optimize documentation before relying on recommendations.
AWS says recommendations are typically delivered within 30–60 minutes, while jobs can take 15 minutes to several hours depending on the data and goals. These are documented expectations, not guaranteed completion times. Recommendations can help narrow options, but validate the selected configuration against your workload’s actual requirements.
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