Elasticsearch Vector Database

What it does

Elasticsearch Vector Database stores text, images, and other data as vector embeddings in an Elasticsearch index and returns ranked results that combine semantic similarity with keyword (BM25) matching, filters, and metadata in a single API call. Documents can be indexed through the semantic_text field, which generates embeddings and handles chunking, or by supplying your own vectors to a dense_vector field.

Teams use it for semantic search, conversational AI, ecommerce discovery, and knowledge search across documents, tickets, and emails. It ships with vector-tuned defaults such as the vectordb_document index mode and Better Binary Quantization, and supports third-party models as well as Jina AI embedding and reranking models run through Elastic Inference Service.

It suits developers and enterprise teams building search or AI retrieval who want vector and keyword search in one system. It is offered as Elastic Cloud serverless, and the broader Elasticsearch platform can also be run on-premises or in air-gapped networks. A free trial is available to get started; serverless is described as usage based, so buyers should confirm current rates on the Elastic pricing page.

What it can do

• Hybrid search combining BM25 keyword search and vector search in a single API call
• Semantic_text field that generates embeddings and handles chunking without a pipeline
• Better Binary Quantization (BBQ) and DiskBBQ for reduced memory use at scale
• Filtered kNN search, filtering, faceting, and aggregations
• Native reranking with Jina reranker models and Learning-to-Rank
• Document- and field-level security with role-based access control

Works with

• Jina AI embedding and reranking models
• Google Cloud and AWS Marketplace listings

Before you choose

• Pricing is usage-based for serverless and resource- or license-based for hosted deployments; check the Elastic pricing page for current rates.
• Some features such as traffic filtering, cross-project search, and bring-your-own-key are described as upcoming rather than available.
• Self-managed and air-gapped deployments carry the operational responsibilities of running your own infrastructure.

Listed under

• Data

Get started

Register for a trial through the Elastic Cloud registration page, or download Elasticsearch to self-manage.

1. Create an Elastic Cloud project or download Elasticsearch, then connect with the Elasticsearch client using your endpoint and API key.
2. Create an index with a semantic_text field, or a dense_vector field if you supply your own embeddings.
3. Index documents with bulk operations, then run semantic or hybrid search queries against the index.

Serverless is described as usage based and can be paid as you go (monthly) or prepaid. Buyers should confirm current meters and rates on the Elastic pricing page. A free trial is offered to get started.

Source: elastic.co/pricing, checked 29 Oct 2026. Prices can change; check before you buy.

Sources for this overview

Product pages, documentation, and pricing checked 29 Oct 2026. Each source supports part of the overview; availability and prices can change.

• World’s most downloaded vector database: Elasticsearch | Elastic ↗www.elastic.co
• Official Elastic Cloud pricing — compare serverless and hosted offerings | Elastic ↗www.elastic.co
• Elastic Docs | Elastic Docs ↗www.elastic.co

Company website

elastic.co: product, pricing and policy pages

Social accounts

Accounts the company links, and its posts on X

Human review

Checked by a person

knowledge_base

manual_review

Last checked 29 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.