Elasticsearch stores documents as JSON and indexes them so searches come back in milliseconds, even across billions of records. It began as a full-text search engine for things like product catalogs and website search, and it is also used heavily for logs, metrics, and security data. It is the core of the Elastic Stack, which adds Kibana for dashboards, and ingestion tools for getting data in. Companies use it wherever they need to search or aggregate a large volume of text-heavy data quickly.
For AI, Elasticsearch works as a vector database and a retrieval layer. It can store embeddings, run semantic and hybrid searches that mix keyword matching with vector similarity, and rerank results before they go to a language model. That makes it a common component of retrieval-augmented generation systems. Query languages such as ES|QL support analytics, and the platform includes tools for connecting to LLMs and building agents on top of company data.
You can self-manage Elasticsearch on your own servers or use Elastic Cloud, in hosted or serverless form. The software is available under open-source licensing options, with some features reserved for paid subscriptions. Running it well takes skill with cluster sizing, indexing, and upgrades, so many teams prefer the managed service. OpenSearch, which is a fork maintained largely by Amazon, Algolia, and Meilisearch are alternatives, and dedicated vector databases such as Pinecone compete for AI use.