Elasticsearch and OpenSearch vector search
Two forked search engines that both added dense vector fields and approximate nearest-neighbour search to an existing full-text search platform.
How Claude rates Elasticsearch and OpenSearch vector search.
Each score below is the model's own, on the rubric it wrote for that list, from what it already knows about Elasticsearch and OpenSearch vector search. Nothing here is edited by us.
The best vector databases for RAG
#10 this week, scoring 6.3 out of 10. Best for Organizations already using Elasticsearch who want to add vector search to existing search infrastructure and workflows.
- Query performance 6
- Functional vector search but slower than dedicated solutions, especially for pure kNN queries without text search.
- Ease of deployment 6
- Easy if already running Elasticsearch, but setting up from scratch is complex with significant operational overhead.
- Metadata filtering 8
- Strong filtering capabilities using Elasticsearch's query DSL, combining vector search with complex boolean logic effectively.
- Scalability & cost 5
- Scales but can be expensive due to resource requirements; not optimized specifically for vector workloads.
- Ecosystem integration 7
- Supported by major RAG frameworks and benefits from Elasticsearch's widespread adoption, though not AI-native.
Rank by week:
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What Elasticsearch and OpenSearch vector search's website sends to your browser.
Scanned 2026-08-25. Grade headers F, scoring 35 out of 100.
This is one narrow, automated check of the HTTP response headers on the vendor's own website. It says nothing about how the product itself is built, how it stores your data, or whether it has ever been audited. A good grade here is a small signal that somebody was paying attention.