Elasticsearch Vector

About

The vendor describes Elasticsearch as a widely deployed, open source vector database. It offers vector search for semantic similarity, hybrid search combining keyword and vector search, and supports both sparse text expansion and dense meaning matches. The page states that Elasticsearch includes features like filters, ranking, and reranking for relevance. It provides hybrid search that integrates lexical search, Jina AI models, geo data, and metadata. The Elastic Inference Service (EIS) offers GPU inference directly in Elasticsearch, supporting Jina AI models and external models via an Inference API. The vendor also highlights Better Binary Quantization (BBQ) for memory footprint reduction up to 95% and a semantic_text field for automated mappings, embeddings, and chunking. An AI Playground is available for testing retrieval and ranking strategies. Elasticsearch can generate and index embeddings, perform kNN search, and apply filters and faceting. It supports combining retrieval methods with hybrid search, including BM25, ELSER, or dense vectors. The product also offers document-level security and compliance authorization with granular role-based access controls. Integrations with Jina AI, Hugging Face, OpenAI, LangChain, LlamaIndex, Amazon Bedrock, Mistral, Google Vertex AI, and Microsoft Azure AI Services are mentioned.

Read from elastic.co on 2026-08-26. We describe what the vendor states; we do not audit it.

Composite of public compliance, deployment, integration, adoption, and company signals. “Not disclosed” reflects gaps in available data, not a vendor deficiency.

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