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Amazon OpenSearch Service offers built-in vector compression, but the right setting depends on where the index runs and what your search workload can tolerate. NextGen Serverless vector collections default to 32× compression and allow several other levels; a separate disk-optimized search mode uses compressed vectors to find candidates, then reads full-precision vectors for rescoring. These are distinct configurations, not one interchangeable compression menu.
What native vector compression does
Quantization stores vectors in a more compact representation than full-precision vectors. That can reduce memory requirements, but it can also affect recall, response time, indexing compatibility, and available query features. Compression is therefore a workload decision rather than a setting with one universally best value.
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OpenSearch Service documentation describes more than one relevant context. NextGen Serverless vector collections have their own compression_level setting. Disk-optimized search is a separate mode with a two-stage query pattern: compressed vectors in memory generate candidates, and full-precision vectors on disk are used for final rescoring. AWS documents different level choices for these contexts, so do not transfer a value from one mode to the other without checking its documentation.
Choose the configuration that matches your deployment
NextGen Serverless vector collections
AWS documents 32× compression as the default for NextGen vector collection indexes. The compression_level setting can be set to 1×, 2×, 8×, 16×, or 32×. NextGen mappings do not require engine or mode parameters because the service selects the configuration internally. AWS also documents a 10-second read-after-write latency through the collection’s refresh_interval; account for that when evaluating how quickly newly written data must become searchable. See AWS’s NextGen vector collection documentation.
One feature limitation is explicit: radial search is unsupported on NextGen vector indexes using 32× compression. If your application relies on radial search, verify its compatibility before choosing that level.
Disk-optimized search
AWS describes disk-optimized search as a cost-oriented option for workloads that do not require single-digit-millisecond response times. Its documentation characterizes P90 latency in the 100–200 ms range as suitable for workloads that can tolerate it; this is guidance for that documented mode, not a service-wide latency guarantee. The mode’s documented compression levels are 2× (FP16), 4× (byte), 8×, 16×, and 32× (binary)—a different list from the NextGen collection choices. Details are in AWS’s cost-optimization guidance.
Weigh memory savings against recall and latency
AWS reports benchmark results for three named datasets. They are AWS-reported results from its cost-optimization documentation, not independent testing or a prediction for another dataset:
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| Dataset | Recall@100 | P90 latency | Reported cost reduction |
|---|---|---|---|
| Cohere TREC-RAG | 0.94 | 104 ms | 83% |
| Tasb-1M | 0.96 | 7 ms | 67% |
| Marco-1M | 0.99 | 7 ms | 67% |
AWS does not state a publication date for these benchmark results. The figures above are those reported in its documentation accessed in 2026, not evidence that the results were first published that year. The same page says default 32× 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 workload.
The differing dataset results illustrate why a compression ratio alone cannot determine suitability. Before migration, establish acceptable recall and P90 latency for your own application, then test with representative vectors, queries, and traffic patterns.
Check indexing acceleration and tuning compatibility
GPU acceleration applies to index building
AWS documents GPU acceleration for vector indexing on OpenSearch domains running version 3.1 or later and on OpenSearch Serverless collections. Its compatibility guidance lists Faiss as supported for GPU acceleration, while Faiss product quantization, IVF, NMSLIB, and Lucene are listed as unsupported for that acceleration. These are index-build acceleration exclusions; they are not blanket statements that those methods or engines cannot be used for vector search. See AWS’s GPU acceleration compatibility documentation.
Auto-optimize recommendations have scope limits
AWS Auto-optimize evaluates configurations against user-defined latency and recall requirements and returns up to three recommendations. The documentation describes support for HNSW-based indexes and lists managed-domain versions 2.19, 3.1, and 3.3, along with engine and regional constraints. AWS says recommendations are typically delivered within 30–60 minutes, while jobs may take 15 minutes to several hours depending on data and goals. These are documented expectations, not completion guarantees. Check the Auto-optimize documentation for current eligibility before planning around it.
Plan for reindexing and validate before production
AWS says applying the documented disk-optimized change to existing indexed data requires reindexing into a new index. That means planning for index rebuild time, temporary storage or capacity, and a controlled cutover—not simply changing a live index setting.
For index codec changes generally, AWS advises testing a representative workload in a non-production environment because changing the codec of an existing index can create challenges. This is general indexing guidance, not a vector-quantization-specific procedure. See AWS’s indexing documentation.
- Identify the exact feature and deployment. Confirm whether you are configuring a NextGen Serverless vector collection or disk-optimized search, and check applicable service, version, engine, and region constraints.
- Set measurable acceptance criteria. Define minimum recall, maximum P90 latency, and the memory or cost reduction that would justify a change.
- Test representative data and queries. Compare the candidate setting with your current index under realistic query patterns, including the query features your application depends on.
- Plan the rebuild and cutover. For the documented disk-optimized migration, create and populate a new index, validate it, then direct production traffic only after results meet your criteria.
How to make the decision
Compare candidate configurations across the factors that affect your particular service and workload:
Quick Recap
- Memory or cost: quantify the benefit in your environment rather than assuming AWS’s benchmark reduction will transfer.
- Search quality: measure recall on representative queries and data.
- Latency: evaluate P90 against the application’s response-time budget.
- Representation and ratio: distinguish the documented compression levels and vector representations for the configuration you are using.
- Build compatibility: check whether your index method and engine support any GPU acceleration you intend to use.
- Query features: confirm the compression setting supports required operations, including radial search where relevant.
- Operations: account for reindexing, rebuild capacity, cutover, and the applicable deployment, version, and regional constraints.
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