TLDR: Elasticsearch is up to 12x faster - We at Elastic have received numerous requests from our community to clarify the performance differences between Elasticsearch and OpenSearch, particularly in the realm of Semantic Search / Vector Search, so we have undertaken this performance testing to provide a clear, data-driven comparison — no ambiguity, just straightforward facts to inform our users. The results show that Elasticsearch is up to 12x faster than OpenSearch for vector search and therefore requires fewer computational resources. This reflects Elastic's focus on consolidating Lucene as the best vector database for search and retrieval use cases.
Vector search is revolutionizing the way we conduct similarity searches, particularly in fields like AI and machine learning. With the increasing adoption of vector embedding models, the ability to efficiently search through millions of high-dimension vectors becomes critical.
When it comes to powering vector databases, Elastic and OpenSearch have taken notably different approaches. Elastic has invested heavily in optimizing Apache Lucene together with Elasticsearch to elevate them as the top-tier choice for vector search applications. In contrast, OpenSearch has broadened its focus, integrating other vector search implementations and exploring beyond Lucene's scope. Our focus on Lucene is strategic, enabling us to provide highly integrated support in our version of Elasticsearch, resulting in an enhanced feature set where each component complements and amplifies the capabilities of the other.
This blog presents a detailed comparison between Elasticsearch 8.14 and OpenSearch 2.14 accounting for different configurations and vector engines. In this performance analysis, Elasticsearch proved to be the superior platform for vector search operations, and upcoming features will widen the differences even more significantly. When pitted against OpenSearch, it excelled in every benchmark track — offering 2x to 12x faster performance on average. This was across scenarios using varying vector amounts and dimensions including so_vector (2M vectors, 768D), openai_vector (2.5M vectors, 1536D), and dense_vector (10M vectors, 96D), all available in this repository alongside the Terraform scripts for provisioning all the required infrastructure on Google Cloud and Kubernetes manifests for running the tests.
The results detailed in this blog complement the results from a previously published and third-party validated study that shows Elasticsearch is 40%–140% faster than OpenSearch for the most common search analytics operations: Text Querying, Sort, Range, Date Histogram and Terms filtering. Now we can add another differentiator: Vector Search.
Up to 12x faster out-of-the-box
Our focused benchmarks across the four vector data sets involved both Approximate KNN and Exact KNN searches, considering different sizes, dimensions and configurations, totaling 40.189.820 uncached search requests. The results: Elasticsearch is up to 12x faster than OpenSearch for vector search and therefore requires fewer computational resources.